Last updated on 2026-10-24 12:51:36 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.0-12 | 1.80 | 42.29 | 44.09 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 2.0-12 | 1.72 | 32.39 | 34.11 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 2.0-12 | 30.38 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 2.0-12 | 30.18 | NOTE | |||
| r-devel-windows-x86_64 | 2.0-12 | 6.00 | 69.00 | 75.00 | NOTE | |
| r-patched-linux-x86_64 | 2.0-12 | 2.20 | 33.36 | 35.56 | OK | |
| r-release-linux-x86_64 | 2.0-12 | 1.78 | 34.15 | 35.93 | OK | |
| r-release-macos-arm64 | 2.0-12 | 1.00 | 12.00 | 13.00 | OK | |
| r-release-macos-x86_64 | 2.0-12 | 2.00 | 38.00 | 40.00 | OK | |
| r-release-windows-x86_64 | 2.0-12 | 4.00 | 63.00 | 67.00 | OK | |
| r-oldrel-macos-arm64 | 2.0-12 | 1.00 | 18.00 | 19.00 | NOTE | |
| r-oldrel-macos-x86_64 | 2.0-12 | 2.00 | 43.00 | 45.00 | NOTE | |
| r-oldrel-windows-x86_64 | 2.0-12 | 6.00 | 67.00 | 73.00 | NOTE |
Version: 2.0-12
Check: tests
Result: NOTE
Running ‘allexercises.R’ [5s/7s]
Comparing ‘allexercises.Rout’ to ‘allexercises.Rout.save’ ...
180c180
< V = 13858, p-value = 4.225e-14
---
> V = 9283.5, p-value = 2.075e-13
471c471
< V = 0, p-value = 0.0452
---
> V = 0, p-value = 0.05906
Running ‘allscripts.R’ [4s/5s]
Comparing ‘allscripts.Rout’ to ‘allscripts.Rout.save’ ... OK
Flavor: r-devel-linux-x86_64-debian-clang
Version: 2.0-12
Check: tests
Result: ERROR
Running ‘allexercises.R’ [3s/4s]
Running ‘allscripts.R’ [3s/4s]
Running the tests in ‘tests/allexercises.R’ failed.
Complete output:
> library(ISwR)
> ps.options(height=3.5, width=4.4, pointsize=8, horiz=F)
> par(mar=c(4,4,3,2)+.1)
> set.seed(310367)
> x <- y <- c(7, 9, NA, NA, 13)
> all(is.na(x) == is.na(y)) & all((x == y)[!is.na(x)])
[1] TRUE
> x <- factor(c("Huey", "Dewey", "Louie", "Huey"))
> y <- c("blue", "red", "green")
> x
[1] Huey Dewey Louie Huey
Levels: Dewey Huey Louie
> y[x]
[1] "red" "blue" "green" "red"
> juul.girl <- juul[juul$age >=7 & juul$age < 14 & juul$sex == 2,]
> summary(juul.girl)
age menarche sex igf1 tanner
Min. : 7.000 Min. :1.000 Min. :2 Min. : 95.0 Min. :1.000
1st Qu.: 8.885 1st Qu.:1.000 1st Qu.:2 1st Qu.:214.2 1st Qu.:1.000
Median :10.560 Median :1.000 Median :2 Median :302.5 Median :1.000
Mean :10.557 Mean :1.132 Mean :2 Mean :351.8 Mean :1.951
3rd Qu.:12.207 3rd Qu.:1.000 3rd Qu.:2 3rd Qu.:473.2 3rd Qu.:3.000
Max. :13.940 Max. :2.000 Max. :2 Max. :914.0 Max. :5.000
NAs :5 NAs :11 NAs :5 NAs :119 NAs :66
testvol
Min. : NA
1st Qu.: NA
Median : NA
Mean :NaN
3rd Qu.: NA
Max. : NA
NAs :351
> sapply(1:10, function(i) mean(rexp(20)))
[1] 0.7969888 0.8648102 0.7280609 1.3195396 1.1868598 1.2246834 0.8794734
[8] 0.9722758 1.0604842 0.8782972
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> x <- 1:10
> z <- append(x, 1.23, after=7)
> z
[1] 1.00 2.00 3.00 4.00 5.00 6.00 7.00 1.23 8.00 9.00 10.00
> z <- c(x[1:7],1.23,x[8:10])
> z
[1] 1.00 2.00 3.00 4.00 5.00 6.00 7.00 1.23 8.00 9.00 10.00
> v <- 1.23; k <- 7
> i <- seq(along=x)
> z <- c(x[i <= k], v, x[i > k])
> z
[1] 1.00 2.00 3.00 4.00 5.00 6.00 7.00 1.23 8.00 9.00 10.00
> write.table(thuesen, file="foo.txt")
> # edit the file
> read.table("foo.txt", na.strings=".")
blood.glucose short.velocity
1 15.3 1.76
2 10.8 1.34
3 8.1 1.27
4 19.5 1.47
5 7.2 1.27
6 5.3 1.49
7 9.3 1.31
8 11.1 1.09
9 7.5 1.18
10 12.2 1.22
11 6.7 1.25
12 5.2 1.19
13 19.0 1.95
14 15.1 1.28
15 6.7 1.52
16 8.6 NA
17 4.2 1.12
18 10.3 1.37
19 12.5 1.19
20 16.1 1.05
21 13.3 1.32
22 4.9 1.03
23 8.8 1.12
24 9.5 1.7
> write.table(thuesen, file="foo.txt", na=".")
> read.table("foo.txt", na.strings=".")
blood.glucose short.velocity
1 15.3 1.76
2 10.8 1.34
3 8.1 1.27
4 19.5 1.47
5 7.2 1.27
6 5.3 1.49
7 9.3 1.31
8 11.1 1.09
9 7.5 1.18
10 12.2 1.22
11 6.7 1.25
12 5.2 1.19
13 19.0 1.95
14 15.1 1.28
15 6.7 1.52
16 8.6 NA
17 4.2 1.12
18 10.3 1.37
19 12.5 1.19
20 16.1 1.05
21 13.3 1.32
22 4.9 1.03
23 8.8 1.12
24 9.5 1.70
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> 1 - pnorm(3)
[1] 0.001349898
> 1 - pnorm(42, mean=35, sd=6)
[1] 0.1216725
> dbinom(10, size=10, prob=0.8)
[1] 0.1073742
> punif(0.9) # this one is obvious...
[1] 0.9
> 1 - pchisq(6.5, df=2)
[1] 0.03877421
> pnorm(-2) * 2
[1] 0.04550026
> qnorm(1-.01/2)
[1] 2.575829
> qnorm(1-.005/2)
[1] 2.807034
> qnorm(1-.001/2)
[1] 3.290527
> qnorm(.25)
[1] -0.6744898
> qnorm(.75)
[1] 0.6744898
> rbinom(10, 1, .5)
[1] 0 1 0 0 0 0 1 0 0 1
> ifelse(rbinom(10, 1, .5) == 1, "H", "T")
[1] "T" "T" "T" "T" "H" "T" "T" "T" "H" "T"
> c("H", "T")[1 + rbinom(10, 1, .5)]
[1] "T" "H" "T" "T" "H" "T" "T" "H" "T" "T"
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> x <- 1:5 ; y <- rexp(5,1) ; opar <- par(mfrow=c(2,2))
> plot(x, y, pch=15) # filled square
> plot(x, y, type="b", lty="dotted")
> plot(x, y, type="b", lwd=3)
> plot(x, y, type="o", col="blue")
> par(opar)
> plot(rnorm(10),type="o", pch=21, bg="white")
> x1 <- rnorm(20)
> x2 <- rnorm(10)+1
> q1 <- qqnorm(x1, plot.it=F)
> q2 <- qqnorm(x2, plot.it=F)
> xr <- range(q1$x, q2$x)
> yr <- range(q1$y, q2$y)
> qqnorm(x1, xlim=xr, ylim=yr)
> points(q2, col="red")
> hist(react)
> library(MASS)
> truehist(react,h=1,x0=.5)
> z <- runif(5)
> curve(quantile(z,x), from=0, to=1)
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> qqnorm(react)
> t.test(react)
One Sample t-test
data: react
t = -7.7512, df = 333, p-value = 1.115e-13
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.9985214 -0.5942930
sample estimates:
mean of x
-0.7964072
> wilcox.test(react, exact=FALSE)
Wilcoxon signed rank test with continuity correction
data: react
V = 13858, p-value = 4.225e-14
alternative hypothesis: true location is not equal to 0
> wilcox.test(vital.capacity~group, data=vitcap, exact=FALSE)
Wilcoxon rank sum test with continuity correction
data: vital.capacity by group
W = 30.5, p-value = 0.01783
alternative hypothesis: true location shift is not equal to 0
> attach(intake) ; opar <- par(mfrow=c(2,2))
> plot(post ~ pre) ; abline(0,1)
> plot((post+pre)/2, post - pre,
+ ylim=range(0,post-pre)); abline(h=0)
> hist(post-pre)
> qqnorm(post-pre)
> detach(intake)
> par(opar)
> shapiro.test(react)
Shapiro-Wilk normality test
data: react
W = 0.95701, p-value = 2.512e-08
> shapiro.test(react[-c(1,334)])
Shapiro-Wilk normality test
data: react[-c(1, 334)]
W = 0.96869, p-value = 1.377e-06
> qqnorm(react[-c(1,334)])
> attach(ashina)
> t.test(vas.active, vas.plac, paired=TRUE)
Paired t-test
data: vas.active and vas.plac
t = -3.2269, df = 15, p-value = 0.005644
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
-71.1946 -14.5554
sample estimates:
mean difference
-42.875
> t.test((vas.active-vas.plac)[grp==1],
+ (vas.plac-vas.active)[grp==2])
Welch Two Sample t-test
data: (vas.active - vas.plac)[grp == 1] and (vas.plac - vas.active)[grp == 2]
t = -3.2517, df = 13.97, p-value = 0.005807
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-130.56481 -26.76853
sample estimates:
mean of x mean of y
-53.50000 25.16667
> t.test(rnorm(25))$p.value #repeat 10x
[1] 0.08718793
> t.test(rt(25,df=2))$p.value #repeat 10x
[1] 0.8077125
> t.test(rexp(25), mu=1)$p.value #repeat 10x
[1] 0.4056805
> x <- replicate(5000, t.test(rexp(25), mu=1)$p.value)
> qqplot(sort(x),ppoints(5000),type="l",log="xy")
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> fit <- lm(metabolic.rate ~ body.weight, data=rmr)
> summary(fit)
Call:
lm(formula = metabolic.rate ~ body.weight, data = rmr)
Residuals:
Min 1Q Median 3Q Max
-245.74 -113.99 -32.05 104.96 484.81
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 811.2267 76.9755 10.539 2.29e-13 ***
body.weight 7.0595 0.9776 7.221 7.03e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 157.9 on 42 degrees of freedom
Multiple R-squared: 0.5539, Adjusted R-squared: 0.5433
F-statistic: 52.15 on 1 and 42 DF, p-value: 7.025e-09
> 811.2267 + 7.0595 * 70 # , or:
[1] 1305.392
> predict(fit, newdata=data.frame(body.weight=70))
1
1305.394
> qt(.975,42)
[1] 2.018082
> 7.0595 + c(-1,1) * 2.018 * 0.9776 # , or:
[1] 5.086703 9.032297
> confint(fit)
2.5 % 97.5 %
(Intercept) 655.883819 966.5695
body.weight 5.086656 9.0324
> summary(lm(log(ab)~age, data=malaria))
Call:
lm(formula = log(ab) ~ age, data = malaria)
Residuals:
Min 1Q Median 3Q Max
-4.0753 -1.0622 0.1181 1.1012 2.7335
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.83697 0.38021 10.092 <2e-16 ***
age 0.10350 0.03954 2.618 0.0103 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.478 on 98 degrees of freedom
Multiple R-squared: 0.06536, Adjusted R-squared: 0.05582
F-statistic: 6.853 on 1 and 98 DF, p-value: 0.01025
> plot(log(ab)~age, data=malaria)
> rho <- .90 ; n <- 100
> x <- rnorm(n)
> y <- rnorm(n, rho * x, sqrt(1 - rho^2))
> plot(x, y)
> cor.test(x, y)
Pearson's product-moment correlation
data: x and y
t = 17.673, df = 98, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.8158420 0.9124999
sample estimates:
cor
0.8724529
> cor.test(x, y, method="spearman")
Spearman's rank correlation rho
data: x and y
S = 23676, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.8579298
> cor.test(x, y, method="kendall")
Kendall's rank correlation tau
data: x and y
z = 9.8457, p-value < 2.2e-16
alternative hypothesis: true tau is not equal to 0
sample estimates:
tau
0.6678788
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> walk <- unlist(zelazo) # or c(..,recursive=TRUE)
> group <- factor(rep(1:4,c(6,6,6,5)), labels=names(zelazo))
> summary(lm(walk ~ group))
Call:
lm(formula = walk ~ group)
Residuals:
Min 1Q Median 3Q Max
-2.7083 -0.8500 -0.3500 0.6375 3.6250
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.1250 0.6191 16.355 1.19e-12 ***
grouppassive 1.2500 0.8755 1.428 0.1696
groupnone 1.5833 0.8755 1.809 0.0864 .
groupctr.8w 2.2250 0.9182 2.423 0.0255 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.516 on 19 degrees of freedom
Multiple R-squared: 0.2528, Adjusted R-squared: 0.1348
F-statistic: 2.142 on 3 and 19 DF, p-value: 0.1285
> t.test(zelazo$active,zelazo$ctr.8w) # first vs. last
Welch Two Sample t-test
data: zelazo$active and zelazo$ctr.8w
t = -3.0449, df = 8.6632, p-value = 0.01453
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.8878619 -0.5621381
sample estimates:
mean of x mean of y
10.125 12.350
> t.test(zelazo$active,unlist(zelazo[-1])) # first vs. rest
Welch Two Sample t-test
data: zelazo$active and unlist(zelazo[-1])
t = -2.386, df = 9.0865, p-value = 0.04058
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.22070952 -0.08811401
sample estimates:
mean of x mean of y
10.12500 11.77941
> fit <- lm(volume~method+subject, data=lung)
> anova(fit)
Analysis of Variance Table
Response: volume
Df Sum Sq Mean Sq F value Pr(>F)
method 2 1.08111 0.54056 6.4953 0.01557 *
subject 5 2.18278 0.43656 5.2457 0.01271 *
Residuals 10 0.83222 0.08322
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> summary(fit)
Call:
lm(formula = volume ~ method + subject, data = lung)
Residuals:
Min 1Q Median 3Q Max
-0.35556 -0.16389 -0.03889 0.17361 0.32778
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.17222 0.19232 16.494 1.4e-08 ***
methodB 0.28333 0.16656 1.701 0.11975
methodC 0.60000 0.16656 3.602 0.00483 **
subject2 -0.83333 0.23555 -3.538 0.00538 **
subject3 0.10000 0.23555 0.425 0.68016
subject4 -0.06667 0.23555 -0.283 0.78293
subject5 -0.03333 0.23555 -0.142 0.89027
subject6 -0.60000 0.23555 -2.547 0.02900 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2885 on 10 degrees of freedom
Multiple R-squared: 0.7968, Adjusted R-squared: 0.6546
F-statistic: 5.603 on 7 and 10 DF, p-value: 0.00768
> kruskal.test(walk ~ group)
Kruskal-Wallis rank sum test
data: walk by group
Kruskal-Wallis chi-squared = 6.8805, df = 3, p-value = 0.0758
> wilcox.test(zelazo$active,zelazo$ctr.8w, exact=FALSE) # first vs. last
Wilcoxon rank sum test with continuity correction
data: zelazo$active and zelazo$ctr.8w
W = 3, p-value = 0.03493
alternative hypothesis: true location shift is not equal to 0
> wilcox.test(zelazo$active,unlist(zelazo[-1]), exact=FALSE) # first vs. rest
Wilcoxon rank sum test with continuity correction
data: zelazo$active and unlist(zelazo[-1])
W = 18, p-value = 0.02224
alternative hypothesis: true location shift is not equal to 0
> friedman.test(volume ~ method | subject, data=lung)
Friedman rank sum test
data: volume and method and subject
Friedman chi-squared = 5.8261, df = 2, p-value = 0.05431
> wilcox.test(lung$volume[lung$method=="A"],
+ lung$volume[lung$method=="C"], paired=TRUE, exact=FALSE) # etc.
Wilcoxon signed rank test with continuity correction
data: lung$volume[lung$method == "A"] and lung$volume[lung$method == "C"]
V = 0, p-value = 0.0452
alternative hypothesis: true location shift is not equal to 0
> attach(juul)
> tapply(sqrt(igf1),tanner, sd, na.rm=TRUE)
1 2 3 4 5
3.203440 3.242068 3.479512 2.566712 3.164611
> plot(sqrt(igf1)~jitter(tanner))
> oneway.test(sqrt(igf1)~tanner)
One-way analysis of means (not assuming equal variances)
data: sqrt(igf1) and tanner
F = 254.17, num df = 4.00, denom df = 162.43, p-value < 2.2e-16
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> binom.test(0, 10, p=.20, alt="less")
Exact binomial test
data: 0 and 10
number of successes = 0, number of trials = 10, p-value = 0.1074
alternative hypothesis: true probability of success is less than 0.2
95 percent confidence interval:
0.0000000 0.2588656
sample estimates:
probability of success
0
> binom.test(0, 13, p=.20, alt="less")
Exact binomial test
data: 0 and 13
number of successes = 0, number of trials = 13, p-value = 0.05498
alternative hypothesis: true probability of success is less than 0.2
95 percent confidence interval:
0.0000000 0.2058167
sample estimates:
probability of success
0
> binom.test(0, 14, p=.20, alt="less")
Exact binomial test
data: 0 and 14
number of successes = 0, number of trials = 14, p-value = 0.04398
alternative hypothesis: true probability of success is less than 0.2
95 percent confidence interval:
0.0000000 0.1926362
sample estimates:
probability of success
0
> prop.test(c(210,122),c(747,661))
2-sample test for equality of proportions with continuity correction
data: c(210, 122) out of c(747, 661)
X-squared = 17.612, df = 1, p-value = 2.709e-05
alternative hypothesis: two.sided
95 percent confidence interval:
0.05138139 0.14172994
sample estimates:
prop 1 prop 2
0.2811245 0.1845688
> M <- matrix(c(23,7,18,13),2,2)
> chisq.test(M)
Pearson's Chi-squared test with Yates' continuity correction
data: M
X-squared = 1.6243, df = 1, p-value = 0.2025
> fisher.test(M)
Fisher's Exact Test for Count Data
data: M
p-value = 0.1737
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.6936416 8.4948588
sample estimates:
odds ratio
2.339104
> prop.test(M)
2-sample test for equality of proportions with continuity correction
data: M
X-squared = 1.6243, df = 1, p-value = 0.2025
alternative hypothesis: two.sided
95 percent confidence interval:
-0.08462185 0.50657307
sample estimates:
prop 1 prop 2
0.5609756 0.3500000
> tbl <- c(42, 157, 47, 62, 4, 15, 4, 1, 8, 28, 9, 7)
> dim(tbl) <- c(2,2,3)
> dimnames(tbl) <- list(c("A","B"),
+ c("not pierced","pierced"),
+ c("ok","broken","cracked"))
> ftable(tbl)
ok broken cracked
A not pierced 42 4 8
pierced 47 4 9
B not pierced 157 15 28
pierced 62 1 7
> fisher.test(tbl["B",,]) # slice analysis
Fisher's Exact Test for Count Data
data: tbl["B", , ]
p-value = 0.1116
alternative hypothesis: two.sided
> fisher.test(tbl["A",,])
Fisher's Exact Test for Count Data
data: tbl["A", , ]
p-value = 1
alternative hypothesis: two.sided
> fisher.test(margin.table(tbl,2:3)) # marginal
Fisher's Exact Test for Count Data
data: margin.table(tbl, 2:3)
p-value = 0.3211
alternative hypothesis: two.sided
> p <- seq(0,1,0.001)
> pval <- sapply(p,function(p)binom.test(3,15,p=p)$p.value)
> plot(p,pval,type="l")
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> power.t.test(power=.8,delta=.30,sd=.20)
Two-sample t test power calculation
n = 8.06031
delta = 0.3
sd = 0.2
sig.level = 0.05
power = 0.8
alternative = two.sided
NOTE: n is number in *each* group
> power.t.test(power=.8,delta=.30,sd=.20,alt="one.sided")
Two-sample t test power calculation
n = 6.298691
delta = 0.3
sd = 0.2
sig.level = 0.05
power = 0.8
alternative = one.sided
NOTE: n is number in *each* group
> (qnorm(.975)+qnorm(.8))^2*2*(.2/.3)^2 # approx. formula
[1] 6.976782
> power.t.test(n=8, delta=.30, sd=.20) # power with eq.size
Two-sample t test power calculation
n = 8
delta = 0.3
sd = 0.2
sig.level = 0.05
power = 0.7965441
alternative = two.sided
NOTE: n is number in *each* group
> d2 <- .30 * sqrt(2/8) / sqrt(1/6+1/10) # corr.f.uneq. size
> power.t.test(n=8, delta=d2, sd=.20)
Two-sample t test power calculation
n = 8
delta = 0.2904738
sd = 0.2
sig.level = 0.05
power = 0.7707066
alternative = two.sided
NOTE: n is number in *each* group
> power.prop.test(power=.9, p1=.6, p2=.75)
Two-sample comparison of proportions power calculation
n = 202.8095
p1 = 0.6
p2 = 0.75
sig.level = 0.05
power = 0.9
alternative = two.sided
NOTE: n is number in *each* group
> power.prop.test(power=.8, p1=.6, p2=.75)
Two-sample comparison of proportions power calculation
n = 151.8689
p1 = 0.6
p2 = 0.75
sig.level = 0.05
power = 0.8
alternative = two.sided
NOTE: n is number in *each* group
> curve(dt(x-3, 25), from=0, to=5)
> curve(dt(x, 25, 3), add=TRUE)
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> attach(thuesen)
> f <- cut(blood.glucose, c(4, 7, 9, 12, 20))
> levels(f) <- c("low", "intermediate", "high", "very high")
> bcmort2 <- within(bcmort,{
+ period <- area <- cohort
+ levels(period) <- rep(c("1991-2001","1981-1991"), each=2)
+ levels(area) <- rep(c("Cph+Frb","Nat"),2)
+ })
> ## Bug on Windows platform gives difference in median for bcmort2$pyr
> ## IGNORE_RDIFF_BEGIN
> summary(bcmort2)
age cohort bc.deaths p.yr period
50-54:4 Study gr. :6 Min. : 9.0 Min. : 25600 1991-2001:12
55-59:4 Nat.ctr. :6 1st Qu.: 51.0 1st Qu.: 86434 1981-1991:12
60-64:4 Hist.ctr. :6 Median :104.0 Median : 169260
65-69:4 Hist.nat.ctr.:6 Mean :213.2 Mean : 396520
70-74:4 3rd Qu.:436.2 3rd Qu.: 781897
75-79:4 Max. :545.0 Max. :1067778
area
Cph+Frb:12
Nat :12
> ## IGNORE_RDIFF_END
> ashina.long <- reshape(ashina, direction="long",
+ varying=1:2, timevar="treat")
> ashina.long <- within(ashina.long, {
+ m <- matrix(c(2,1,1,2),2)
+ id <- factor(id)
+ treat <- factor(treat)
+ grp <- factor(grp)
+ period <- factor(m[cbind(grp,treat)])
+ rm(m)
+ })
> within(ashina.long,
+ period2 <- ifelse(treat != "active",
+ as.numeric(grp), 3 - as.numeric(grp))
+ )
grp treat vas id period period2
1.active 1 active -167 1 2 2
2.active 1 active -127 2 2 2
3.active 1 active -58 3 2 2
4.active 1 active -103 4 2 2
5.active 1 active -35 5 2 2
6.active 1 active -164 6 2 2
7.active 1 active -3 7 2 2
8.active 1 active 25 8 2 2
9.active 1 active -61 9 2 2
10.active 1 active -45 10 2 2
11.active 2 active -38 11 1 1
12.active 2 active 29 12 1 1
13.active 2 active 2 13 1 1
14.active 2 active -18 14 1 1
15.active 2 active -74 15 1 1
16.active 2 active -72 16 1 1
1.plac 1 plac -102 1 1 1
2.plac 1 plac -39 2 1 1
3.plac 1 plac 32 3 1 1
4.plac 1 plac 28 4 1 1
5.plac 1 plac 16 5 1 1
6.plac 1 plac -42 6 1 1
7.plac 1 plac -27 7 1 1
8.plac 1 plac -30 8 1 1
9.plac 1 plac -47 9 1 1
10.plac 1 plac 8 10 1 1
11.plac 2 plac 12 11 2 2
12.plac 2 plac 11 12 2 2
13.plac 2 plac -9 13 2 2
14.plac 2 plac -1 14 2 2
15.plac 2 plac 3 15 2 2
16.plac 2 plac -36 16 2 2
> stroke.trim <- function(t1, t2)
+ subset(transform(stroke,
+ entry=t1, exit=pmin(t2, obsmonths),
+ dead=dead & obsmonths <= t2),
+ entry < exit)
> stroke2 <- do.call(rbind, mapply(stroke.trim,
+ c(0,0.5,2,12), c(0.5,2,12,Inf), SIMPLIFY=F))
> table(stroke$dead)
FALSE TRUE
344 485
> table(stroke2$dead)
FALSE TRUE
1956 485
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> summary(lm(log(bwt) ~ log(bpd) + log(ad), data=secher))
Call:
lm(formula = log(bwt) ~ log(bpd) + log(ad), data = secher)
Residuals:
Min 1Q Median 3Q Max
-0.35074 -0.06741 -0.00792 0.05750 0.36360
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.8615 0.6617 -8.859 2.36e-14 ***
log(bpd) 1.5519 0.2294 6.764 8.09e-10 ***
log(ad) 1.4667 0.1467 9.998 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1068 on 104 degrees of freedom
Multiple R-squared: 0.8583, Adjusted R-squared: 0.8556
F-statistic: 314.9 on 2 and 104 DF, p-value: < 2.2e-16
> summary(lm(log(bwt) ~ log(ad), data=secher))
Call:
lm(formula = log(bwt) ~ log(ad), data = secher)
Residuals:
Min 1Q Median 3Q Max
-0.58560 -0.06609 0.00184 0.07479 0.48435
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.4446 0.5103 -4.791 5.49e-06 ***
log(ad) 2.2365 0.1105 20.238 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1275 on 105 degrees of freedom
Multiple R-squared: 0.7959, Adjusted R-squared: 0.794
F-statistic: 409.6 on 1 and 105 DF, p-value: < 2.2e-16
> pairs(tlc)
> summary(lm(log(tlc) ~ ., data=tlc))
Call:
lm(formula = log(tlc) ~ ., data = tlc)
Residuals:
Min 1Q Median 3Q Max
-0.32648 -0.12520 0.02708 0.12424 0.42314
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.157899 0.565698 -2.047 0.050163 .
age -0.003647 0.003864 -0.944 0.353272
sex 0.086184 0.082015 1.051 0.302324
height 0.017328 0.004032 4.298 0.000188 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1898 on 28 degrees of freedom
Multiple R-squared: 0.5927, Adjusted R-squared: 0.549
F-statistic: 13.58 on 3 and 28 DF, p-value: 1.181e-05
> opar <- par(mfrow=c(2,2))
> plot(lm(log(tlc) ~ ., data=tlc), which=1:4)
>
> drop1(lm(log(tlc) ~ ., data=tlc))
Single term deletions
Model:
log(tlc) ~ age + sex + height
Df Sum of Sq RSS AIC
<none> 1.0087 -102.626
age 1 0.03210 1.0408 -103.623
sex 1 0.03978 1.0485 -103.388
height 1 0.66546 1.6742 -88.413
> drop1(lm(log(tlc) ~ . - age, data=tlc))
Single term deletions
Model:
log(tlc) ~ (age + sex + height) - age
Df Sum of Sq RSS AIC
<none> 1.0408 -103.623
sex 1 0.05132 1.0921 -104.083
height 1 0.71317 1.7540 -88.923
>
> par(mfrow=c(1,1))
> plot(log(tlc) ~ height, data=tlc)
> par(mfrow=c(2,2))
> plot(lm(tlc ~ ., data=tlc), which=1:4) # slightly worse
> par(opar)
> summary(lm(sqrt(igf1) ~ age, data=juul2, subset=(age >= 25)))
Call:
lm(formula = sqrt(igf1) ~ age, data = juul2, subset = (age >=
25))
Residuals:
Min 1Q Median 3Q Max
-4.936 -1.184 0.113 1.036 4.021
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.89742 0.46904 40.29 <2e-16 ***
age -0.10871 0.01034 -10.52 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.742 on 126 degrees of freedom
(9 observations deleted due to missingness)
Multiple R-squared: 0.4676, Adjusted R-squared: 0.4633
F-statistic: 110.6 on 1 and 126 DF, p-value: < 2.2e-16
> anova(lm(sqrt(igf1) ~ age + weight + height,
+ data=juul2, subset=(age >= 25)))
Analysis of Variance Table
Response: sqrt(igf1)
Df Sum Sq Mean Sq F value Pr(>F)
age 1 16.386 16.3861 3.7610 0.06132 .
weight 1 2.454 2.4536 0.5632 0.45848
height 1 0.004 0.0036 0.0008 0.97715
Residuals 32 139.419 4.3568
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> summary(lm(dl.milk ~ . - no, data=kfm))
Call:
lm(formula = dl.milk ~ . - no, data = kfm)
Residuals:
Min 1Q Median 3Q Max
-1.74201 -0.81173 -0.00926 0.78326 2.52646
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -11.681839 4.361561 -2.678 0.010363 *
sexgirl -0.499532 0.312672 -1.598 0.117284
weight 1.349124 0.322450 4.184 0.000135 ***
ml.suppl -0.002233 0.001241 -1.799 0.078829 .
mat.weight 0.006212 0.023708 0.262 0.794535
mat.height 0.072278 0.030169 2.396 0.020906 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.075 on 44 degrees of freedom
Multiple R-squared: 0.5459, Adjusted R-squared: 0.4943
F-statistic: 10.58 on 5 and 44 DF, p-value: 1.03e-06
> summary(lm(dl.milk ~ . - no - mat.weight, data=kfm))
Call:
lm(formula = dl.milk ~ . - no - mat.weight, data = kfm)
Residuals:
Min 1Q Median 3Q Max
-1.77312 -0.81196 -0.00683 0.76988 2.52240
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -12.112571 3.997860 -3.030 0.00405 **
sexgirl -0.494675 0.308875 -1.602 0.11626
weight 1.372524 0.306612 4.476 5.14e-05 ***
ml.suppl -0.002313 0.001190 -1.943 0.05824 .
mat.height 0.076363 0.025560 2.988 0.00454 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.064 on 45 degrees of freedom
Multiple R-squared: 0.5452, Adjusted R-squared: 0.5047
F-statistic: 13.48 on 4 and 45 DF, p-value: 2.658e-07
> summary(lm(dl.milk ~ . - no - mat.weight - sex, data=kfm))
Call:
lm(formula = dl.milk ~ . - no - mat.weight - sex, data = kfm)
Residuals:
Min 1Q Median 3Q Max
-2.06540 -0.74758 -0.02408 0.67488 2.79882
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -13.064926 4.020073 -3.250 0.00216 **
weight 1.464781 0.306231 4.783 1.81e-05 ***
ml.suppl -0.002237 0.001209 -1.850 0.07074 .
mat.height 0.077600 0.025979 2.987 0.00451 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.082 on 46 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.4879
F-statistic: 16.56 on 3 and 46 DF, p-value: 1.953e-07
> summary(lm(dl.milk ~ weight + mat.height, data=kfm))
Call:
lm(formula = dl.milk ~ weight + mat.height, data = kfm)
Residuals:
Min 1Q Median 3Q Max
-2.19598 -0.82149 0.01822 0.75582 2.83375
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -11.92014 4.07325 -2.926 0.00527 **
weight 1.42862 0.31338 4.559 3.67e-05 ***
mat.height 0.07063 0.02636 2.680 0.01013 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.109 on 47 degrees of freedom
Multiple R-squared: 0.4835, Adjusted R-squared: 0.4615
F-statistic: 22 on 2 and 47 DF, p-value: 1.811e-07
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> ashina.long <- reshape(ashina, direction="long",
+ varying=1:2, timevar="treat")
> ashina.long <- within(ashina.long, {
+ m <- matrix(c(2,1,1,2),2)
+ id <- factor(id)
+ treat <- factor(treat)
+ grp <- factor(grp)
+ period <- factor(m[cbind(grp,treat)])
+ rm(m)
+ })
> fit.ashina <- lm(vas ~ id + period + treat, data=ashina.long)
> ## The following gets into trouble with CRAN testing due to a Sum Sq
> ## which is theoretically 51137.5 so should round-to-even 51138 in the display,
> ## but on some platforms it is slightly smaller and rounds down.
> zapsmall(drop1(fit.ashina, test="F"), digits=10)
Single term deletions
Model:
vas ~ id + period + treat
Df Sum of Sq RSS AIC F value Pr(>F)
<none> 19679 241.49
id 15 51138 70816 252.47 2.4254 0.05287 .
period 1 1505 21184 241.85 1.0709 0.31830
treat 1 11603 31282 254.32 8.2550 0.01228 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> zapsmall(anova(fit.ashina), digits=10)
Analysis of Variance Table
Response: vas
Df Sum Sq Mean Sq F value Pr(>F)
id 15 51138 3409.2 2.4254 0.05287 .
period 1 4608 4608.0 3.2783 0.09171 .
treat 1 11603 11603.3 8.2550 0.01228 *
Residuals 14 19679 1405.6
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> attach(ashina)
> dd <- vas.active - vas.plac
> t.test(dd[grp==1], -dd[grp==2], var.eq=T)
Two Sample t-test
data: dd[grp == 1] and -dd[grp == 2]
t = -2.8731, df = 14, p-value = 0.01228
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-137.39089 -19.94244
sample estimates:
mean of x mean of y
-53.50000 25.16667
> t.test(dd[grp==1], dd[grp==2], var.eq=T)
Two Sample t-test
data: dd[grp == 1] and dd[grp == 2]
t = -1.0348, df = 14, p-value = 0.3183
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-87.05756 30.39089
sample estimates:
mean of x mean of y
-53.50000 -25.16667
> attach(tb.dilute)
> anova(lm(reaction ~ animal + logdose))
Analysis of Variance Table
Response: reaction
Df Sum Sq Mean Sq F value Pr(>F)
animal 5 35.208 7.042 8.2641 0.002527 **
logdose 2 72.396 36.198 42.4817 1.295e-05 ***
Residuals 10 8.521 0.852
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> ld <- c(0.5, 0, -0.5)[logdose]
> anova(lm(reaction ~ animal + ld))
Analysis of Variance Table
Response: reaction
Df Sum Sq Mean Sq F value Pr(>F)
animal 5 35.208 7.042 6.4353 0.00496 **
ld 1 68.880 68.880 62.9489 7.067e-06 ***
Residuals 11 12.036 1.094
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> summary(lm(reaction ~ animal + ld))
Call:
lm(formula = reaction ~ animal + ld)
Residuals:
Min 1Q Median 3Q Max
-1.72917 -0.22917 -0.08333 0.23437 2.41667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.0000 0.6039 18.214 1.45e-09 ***
animal2 1.0000 0.8541 1.171 0.266408
animal3 2.1667 0.8541 2.537 0.027637 *
animal4 2.1667 0.8541 2.537 0.027637 *
animal5 3.5833 0.8541 4.195 0.001497 **
animal6 4.0833 0.8541 4.781 0.000571 ***
ld 4.7917 0.6039 7.934 7.07e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.046 on 11 degrees of freedom
Multiple R-squared: 0.8963, Adjusted R-squared: 0.8398
F-statistic: 15.85 on 6 and 11 DF, p-value: 7.816e-05
> 4.7917 + 0.6039 * qt(c(.025,.975), 11)
[1] 3.462525 6.120875
> # or:
> confint(lm(reaction ~ animal + ld))["ld",]
2.5 % 97.5 %
3.462408 6.120925
>
> slopes <- reaction[logdose==0.5] - reaction[logdose==-0.5]
> t.test(slopes)
One Sample t-test
data: slopes
t = 16.429, df = 5, p-value = 1.525e-05
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
4.041914 5.541420
sample estimates:
mean of x
4.791667
>
> anova(lm(reaction ~ animal*ld))
Analysis of Variance Table
Response: reaction
Df Sum Sq Mean Sq F value Pr(>F)
animal 5 35.208 7.042 3.9264 0.0630805 .
ld 1 68.880 68.880 38.4076 0.0008133 ***
animal:ld 5 1.276 0.255 0.1423 0.9753664
Residuals 6 10.760 1.793
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> a <- gl(2, 2, 8)
> b <- gl(2, 4, 8)
> x <- 1:8
> y <- c(1:4,8:5)
> z <- rnorm(8)
> model.matrix(~ a:b) ; lm(z ~ a:b)
(Intercept) a1:b1 a2:b1 a1:b2 a2:b2
1 1 1 0 0 0
2 1 1 0 0 0
3 1 0 1 0 0
4 1 0 1 0 0
5 1 0 0 1 0
6 1 0 0 1 0
7 1 0 0 0 1
8 1 0 0 0 1
attr(,"assign")
[1] 0 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$a
[1] "contr.treatment"
attr(,"contrasts")$b
[1] "contr.treatment"
Call:
lm(formula = z ~ a:b)
Coefficients:
(Intercept) a1:b1 a2:b1 a1:b2 a2:b2
0.2537 -0.3222 -0.1409 -0.6558 NA
> model.matrix(~ a * b) ; lm(z ~ a * b)
(Intercept) a2 b2 a2:b2
1 1 0 0 0
2 1 0 0 0
3 1 1 0 0
4 1 1 0 0
5 1 0 1 0
6 1 0 1 0
7 1 1 1 1
8 1 1 1 1
attr(,"assign")
[1] 0 1 2 3
attr(,"contrasts")
attr(,"contrasts")$a
[1] "contr.treatment"
attr(,"contrasts")$b
[1] "contr.treatment"
Call:
lm(formula = z ~ a * b)
Coefficients:
(Intercept) a2 b2 a2:b2
-0.06848 0.18134 -0.33360 0.47449
> model.matrix(~ a:x) ; lm(z ~ a:x)
(Intercept) a1:x a2:x
1 1 1 0
2 1 2 0
3 1 0 3
4 1 0 4
5 1 5 0
6 1 6 0
7 1 0 7
8 1 0 8
attr(,"assign")
[1] 0 1 1
attr(,"contrasts")
attr(,"contrasts")$a
[1] "contr.treatment"
Call:
lm(formula = z ~ a:x)
Coefficients:
(Intercept) a1:x a2:x
0.12999 -0.09671 0.00482
> model.matrix(~ a * x) ; lm(z ~ a * x)
(Intercept) a2 x a2:x
1 1 0 1 0
2 1 0 2 0
3 1 1 3 3
4 1 1 4 4
5 1 0 5 0
6 1 0 6 0
7 1 1 7 7
8 1 1 8 8
attr(,"assign")
[1] 0 1 2 3
attr(,"contrasts")
attr(,"contrasts")$a
[1] "contr.treatment"
Call:
lm(formula = z ~ a * x)
Coefficients:
(Intercept) a2 x a2:x
0.02594 0.32161 -0.07464 0.04477
> model.matrix(~ b * (x + y)) ; lm(z ~ b * (x + y))
(Intercept) b2 x y b2:x b2:y
1 1 0 1 1 0 0
2 1 0 2 2 0 0
3 1 0 3 3 0 0
4 1 0 4 4 0 0
5 1 1 5 8 5 8
6 1 1 6 7 6 7
7 1 1 7 6 7 6
8 1 1 8 5 8 5
attr(,"assign")
[1] 0 1 2 3 4 5
attr(,"contrasts")
attr(,"contrasts")$b
[1] "contr.treatment"
Call:
lm(formula = z ~ b * (x + y))
Coefficients:
(Intercept) b2 x y b2:x b2:y
0.002029 -0.128603 0.066866 -0.058803 NA NA
> attach(secretin)
> model1 <- lm(gluc ~ person * time)
> model2 <- lm(gluc ~ person + time)
> model3 <- lm(gluc ~ person * time20plus + time)
> model4 <- lm(gluc ~ person * time20plus + time.comb)
> tt <- c(20,30,60,90,0)[time]
> plot(fitted(model4)~tt,pch=as.character(person))
> bp.obese <- transform(bp.obese,sex=factor(sex, labels=c("M","F")))
> plot(log(bp) ~ log(obese), pch=c(20,21)[sex], data=bp.obese)
> summary(lm(log(bp) ~ sex, data=bp.obese))
Call:
lm(formula = log(bp) ~ sex, data = bp.obese)
Residuals:
Min 1Q Median 3Q Max
-0.30067 -0.07468 -0.01583 0.09436 0.50927
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.84396 0.02037 237.851 <2e-16 ***
sexF -0.01569 0.02701 -0.581 0.563
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1351 on 100 degrees of freedom
Multiple R-squared: 0.003365, Adjusted R-squared: -0.006601
F-statistic: 0.3376 on 1 and 100 DF, p-value: 0.5625
> summary(lm(log(bp) ~ sex + log(obese), data=bp.obese))
Call:
lm(formula = log(bp) ~ sex + log(obese), data = bp.obese)
Residuals:
Min 1Q Median 3Q Max
-0.21260 -0.09068 -0.00472 0.05675 0.43846
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.79180 0.02249 213.05 < 2e-16 ***
sexF -0.06393 0.02744 -2.33 0.0218 *
log(obese) 0.31235 0.07366 4.24 5.02e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1249 on 99 degrees of freedom
Multiple R-squared: 0.1565, Adjusted R-squared: 0.1395
F-statistic: 9.187 on 2 and 99 DF, p-value: 0.0002188
> summary(lm(log(bp) ~ sex*log(obese), data=bp.obese))
Call:
lm(formula = log(bp) ~ sex * log(obese), data = bp.obese)
Residuals:
Min 1Q Median 3Q Max
-0.21455 -0.09020 -0.00741 0.05710 0.42968
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.80589 0.02896 165.930 <2e-16 ***
sexF -0.09047 0.04393 -2.059 0.0421 *
log(obese) 0.22798 0.13159 1.733 0.0863 .
sexF:log(obese) 0.12310 0.15895 0.774 0.4405
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1252 on 98 degrees of freedom
Multiple R-squared: 0.1617, Adjusted R-squared: 0.136
F-statistic: 6.3 on 3 and 98 DF, p-value: 0.0005933
> vitcap2 <- transform(vitcap2,group=factor(group,
+ labels=c("exp>10",
+ "exp<10", "unexp")))
> attach(vitcap2)
> plot(vital.capacity~age, pch=(20:22)[group])
> vit.fit <- lm(vital.capacity ~ age*group)
> summary(vit.fit)
Call:
lm(formula = vital.capacity ~ age * group)
Residuals:
Min 1Q Median 3Q Max
-1.24497 -0.36929 0.01977 0.43681 1.13953
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.18344 0.99358 8.236 3.28e-12 ***
age -0.08511 0.01967 -4.327 4.44e-05 ***
groupexp<10 -1.95341 1.10481 -1.768 0.0810 .
groupunexp -2.50315 1.04184 -2.403 0.0187 *
age:groupexp<10 0.03858 0.02327 1.658 0.1014
age:groupunexp 0.05450 0.02107 2.587 0.0116 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5942 on 78 degrees of freedom
Multiple R-squared: 0.422, Adjusted R-squared: 0.385
F-statistic: 11.39 on 5 and 78 DF, p-value: 2.871e-08
> drop1(vit.fit, test="F")
Single term deletions
Model:
vital.capacity ~ age * group
Df Sum of Sq RSS AIC F value Pr(>F)
<none> 27.535 -81.689
age:group 2 2.4995 30.035 -78.391 3.5402 0.03376 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> for (i in 1:3) abline(lm(vital.capacity ~ age,
+ subset=as.numeric(group)==i), lty=i)
> legend(20, 3.5 ,legend=levels(group), pch=20:22, lty=1:3)
> juul.prepub <- subset(juul, tanner==1)
>
> summary(lm(sqrt(igf1)~age, data=juul.prepub, subset= sex==1))
Call:
lm(formula = sqrt(igf1) ~ age, data = juul.prepub, subset = sex ==
1)
Residuals:
Min 1Q Median 3Q Max
-7.8319 -1.7113 -0.1784 1.4845 11.1109
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.50382 0.46198 18.41 <2e-16 ***
age 0.64564 0.05357 12.05 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.678 on 190 degrees of freedom
(99 observations deleted due to missingness)
Multiple R-squared: 0.4333, Adjusted R-squared: 0.4303
F-statistic: 145.3 on 1 and 190 DF, p-value: < 2.2e-16
> summary(lm(sqrt(igf1)~age, data=juul.prepub, subset= sex==2))
Call:
lm(formula = sqrt(igf1) ~ age, data = juul.prepub, subset = sex ==
2)
Residuals:
Min 1Q Median 3Q Max
-6.2532 -1.3766 -0.0526 1.1144 6.1815
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.7055 0.9047 11.833 < 2e-16 ***
age 0.4761 0.1020 4.667 8.18e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.18 on 117 degrees of freedom
(105 observations deleted due to missingness)
Multiple R-squared: 0.157, Adjusted R-squared: 0.1498
F-statistic: 21.79 on 1 and 117 DF, p-value: 8.184e-06
>
> summary(lm(sqrt(igf1)~age*factor(sex), data=juul.prepub))
Call:
lm(formula = sqrt(igf1) ~ age * factor(sex), data = juul.prepub)
Residuals:
Min 1Q Median 3Q Max
-7.832 -1.586 -0.085 1.337 11.111
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.50382 0.43127 19.718 <2e-16 ***
age 0.64564 0.05001 12.911 <2e-16 ***
factor(sex)2 2.20169 1.12350 1.960 0.0509 .
age:factor(sex)2 -0.16952 0.12721 -1.333 0.1836
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.5 on 307 degrees of freedom
(204 observations deleted due to missingness)
Multiple R-squared: 0.3969, Adjusted R-squared: 0.391
F-statistic: 67.33 on 3 and 307 DF, p-value: < 2.2e-16
> summary(lm(sqrt(igf1)~age+factor(sex), data=juul.prepub))
Call:
lm(formula = sqrt(igf1) ~ age + factor(sex), data = juul.prepub)
Residuals:
Min 1Q Median 3Q Max
-7.7224 -1.5711 -0.0889 1.3310 11.1234
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.70903 0.40335 21.59 <2e-16 ***
age 0.61945 0.04604 13.45 <2e-16 ***
factor(sex)2 0.75669 0.29445 2.57 0.0106 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.503 on 308 degrees of freedom
(204 observations deleted due to missingness)
Multiple R-squared: 0.3934, Adjusted R-squared: 0.3894
F-statistic: 99.86 on 2 and 308 DF, p-value: < 2.2e-16
> summary(fit.aicopt <- step(lm(dl.milk ~ . - no, data=kfm)))
Start: AIC=12.83
dl.milk ~ (no + sex + weight + ml.suppl + mat.weight + mat.height) -
no
Df Sum of Sq RSS AIC
- mat.weight 1 0.0793 50.919 10.911
<none> 50.840 12.833
- sex 1 2.9492 53.789 13.652
- ml.suppl 1 3.7408 54.580 14.383
- mat.height 1 6.6318 57.471 16.963
- weight 1 20.2269 71.067 27.580
Step: AIC=10.91
dl.milk ~ sex + weight + ml.suppl + mat.height
Df Sum of Sq RSS AIC
<none> 50.919 10.911
- sex 1 2.9023 53.821 11.682
- ml.suppl 1 4.2735 55.193 12.940
- mat.height 1 10.0999 61.019 17.958
- weight 1 22.6741 73.593 27.326
Call:
lm(formula = dl.milk ~ sex + weight + ml.suppl + mat.height,
data = kfm)
Residuals:
Min 1Q Median 3Q Max
-1.77312 -0.81196 -0.00683 0.76988 2.52240
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -12.112571 3.997860 -3.030 0.00405 **
sexgirl -0.494675 0.308875 -1.602 0.11626
weight 1.372524 0.306612 4.476 5.14e-05 ***
ml.suppl -0.002313 0.001190 -1.943 0.05824 .
mat.height 0.076363 0.025560 2.988 0.00454 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.064 on 45 degrees of freedom
Multiple R-squared: 0.5452, Adjusted R-squared: 0.5047
F-statistic: 13.48 on 4 and 45 DF, p-value: 2.658e-07
> opar <- par(mfrow=c(2,2))
> plot(fit.aicopt, which=1:4)
> kfm[32,]
no dl.milk sex weight ml.suppl mat.weight mat.height
32 37 7.22 girl 5.336 590 58 160
> summary(kfm)
no dl.milk sex weight ml.suppl
Min. : 1.00 Min. : 4.440 boy :25 Min. :4.120 Min. : 0.00
1st Qu.: 28.75 1st Qu.: 6.555 girl:25 1st Qu.:4.977 1st Qu.: 16.25
Median : 50.50 Median : 7.660 Median :5.352 Median : 57.50
Mean : 52.36 Mean : 7.504 Mean :5.319 Mean : 96.00
3rd Qu.: 79.75 3rd Qu.: 8.428 3rd Qu.:5.637 3rd Qu.:103.75
Max. :105.00 Max. :10.430 Max. :6.578 Max. :590.00
mat.weight mat.height
Min. :47.00 Min. :153.0
1st Qu.:55.00 1st Qu.:162.0
Median :58.00 Median :167.0
Mean :59.96 Mean :167.4
3rd Qu.:64.50 3rd Qu.:172.0
Max. :80.00 Max. :185.0
> summary(update(fit.aicopt, ~ . - sex))
Call:
lm(formula = dl.milk ~ weight + ml.suppl + mat.height, data = kfm)
Residuals:
Min 1Q Median 3Q Max
-2.06540 -0.74758 -0.02408 0.67488 2.79882
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -13.064926 4.020073 -3.250 0.00216 **
weight 1.464781 0.306231 4.783 1.81e-05 ***
ml.suppl -0.002237 0.001209 -1.850 0.07074 .
mat.height 0.077600 0.025979 2.987 0.00451 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.082 on 46 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.4879
F-statistic: 16.56 on 3 and 46 DF, p-value: 1.953e-07
> plot(update(fit.aicopt, ~ . - sex - ml.suppl), which=1:4)
> par(opar)
> juulyoung <- subset(juul, age < 25)
> juulyoung <- transform(juulyoung,
+ sex=factor(sex), tanner=factor(tanner))
> fit.untf <- lm(igf1 ~ age * sex * tanner, data=juulyoung,
+ na.action=na.exclude)
> plot(fitted(fit.untf) ~ age, data=juulyoung,
+ col=c("red","green")[sex])
> fit.log <- update(fit.untf, log(igf1) ~ .)
> fit.sqrt <- update(fit.untf, sqrt(igf1) ~ .)
> opar <- par(mfrow=c(2,2))
> plot(fit.untf, which=1:4)
> plot(fit.log, which=1:4)
> plot(fit.sqrt, which=1:4)
> par(opar)
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> summary(glm(mal~age+log(ab), binomial, data=malaria))
Call:
glm(formula = mal ~ age + log(ab), family = binomial, data = malaria)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.57234 0.95184 2.702 0.006883 **
age -0.06546 0.06772 -0.967 0.333703
log(ab) -0.68235 0.19552 -3.490 0.000483 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 116.652 on 99 degrees of freedom
Residual deviance: 98.017 on 97 degrees of freedom
AIC: 104.02
Number of Fisher Scoring iterations: 5
> attach(graft.vs.host)
> type <- factor(type,labels=c("AML", "ALL", "CML"))
> m1 <- glm(gvhd~rcpage+donage+type+preg+log(index), binomial)
> m1a <- glm(gvhd~rcpage+donage+type+preg+index, binomial)
> summary(m1)
Call:
glm(formula = gvhd ~ rcpage + donage + type + preg + log(index),
family = binomial)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.47420 2.59647 -2.108 0.0350 *
rcpage 0.01597 0.08205 0.195 0.8457
donage 0.10724 0.08262 1.298 0.1943
typeALL -0.42824 1.25389 -0.342 0.7327
typeCML 1.61517 1.30743 1.235 0.2167
preg 1.66236 1.18103 1.408 0.1593
log(index) 1.81874 0.88919 2.045 0.0408 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 51.049 on 36 degrees of freedom
Residual deviance: 26.252 on 30 degrees of freedom
AIC: 40.252
Number of Fisher Scoring iterations: 6
> summary(m1a)
Call:
glm(formula = gvhd ~ rcpage + donage + type + preg + index, family = binomial)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -6.08898 2.67548 -2.276 0.0229 *
rcpage 0.03273 0.08224 0.398 0.6906
donage 0.09267 0.07974 1.162 0.2452
typeALL -0.37184 1.26596 -0.294 0.7690
typeCML 1.80883 1.34681 1.343 0.1793
preg 1.92847 1.18157 1.632 0.1027
index 0.68422 0.38065 1.797 0.0723 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 51.049 on 36 degrees of freedom
Residual deviance: 25.964 on 30 degrees of freedom
AIC: 39.964
Number of Fisher Scoring iterations: 6
> drop1(m1, test="Chisq")
Single term deletions
Model:
gvhd ~ rcpage + donage + type + preg + log(index)
Df Deviance AIC LRT Pr(>Chi)
<none> 26.252 40.252
rcpage 1 26.291 38.291 0.0382 0.84495
donage 1 28.082 40.082 1.8292 0.17622
type 2 28.959 38.959 2.7064 0.25841
preg 1 28.447 40.447 2.1947 0.13849
log(index) 1 32.343 44.343 6.0904 0.01359 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> drop1(update(m1, ~ . - rcpage), test="Chisq")
Single term deletions
Model:
gvhd ~ donage + type + preg + log(index)
Df Deviance AIC LRT Pr(>Chi)
<none> 26.291 38.291
donage 1 28.832 38.832 2.5411 0.11092
type 2 29.403 37.403 3.1126 0.21092
preg 1 28.812 38.812 2.5218 0.11228
log(index) 1 32.630 42.630 6.3397 0.01181 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> drop1(update(m1, ~ . - rcpage - type), test="Chisq")
Single term deletions
Model:
gvhd ~ donage + preg + log(index)
Df Deviance AIC LRT Pr(>Chi)
<none> 29.403 37.403
donage 1 33.654 39.654 4.2506 0.0392375 *
preg 1 31.068 37.068 1.6653 0.1968922
log(index) 1 41.723 47.723 12.3198 0.0004482 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> drop1(update(m1, ~ . - rcpage - type - preg), test="Chisq")
Single term deletions
Model:
gvhd ~ donage + log(index)
Df Deviance AIC LRT Pr(>Chi)
<none> 31.068 37.068
donage 1 37.740 41.740 6.671 0.0097993 **
log(index) 1 45.476 49.476 14.407 0.0001473 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> summary(m2 <- glm(gvhd~donage + log(index), binomial))
Call:
glm(formula = gvhd ~ donage + log(index), family = binomial)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.45399 2.08147 -2.620 0.00879 **
donage 0.14594 0.06465 2.257 0.02399 *
log(index) 2.17773 0.78986 2.757 0.00583 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 51.049 on 36 degrees of freedom
Residual deviance: 31.068 on 34 degrees of freedom
AIC: 37.068
Number of Fisher Scoring iterations: 5
> confint(m2)
Waiting for profiling to be done...
2.5 % 97.5 %
(Intercept) -10.38849705 -1.9550010
donage 0.03262764 0.2946854
log(index) 0.89146245 4.0706703
> ## normal approximation:
> est <- coefficients(summary(m2))[,1]
> se <- coefficients(summary(m2))[,2]
> est + cbind(qnorm(.025)*se, qnorm(.975)*se)
[,1] [,2]
(Intercept) -9.53358695 -1.3743858
donage 0.01922664 0.2726562
log(index) 0.62964464 3.7258196
> confint.default(m2)
2.5 % 97.5 %
(Intercept) -9.53358695 -1.3743858
donage 0.01922664 0.2726562
log(index) 0.62964464 3.7258196
> counts <- c(13,40,157,40,21,61)
> total <- c(108,264,375,310,181,162)
> age <- gl(3,1,6)
> type <- gl(2,3,6)
> anova(glm(counts/total~age+type,weights=total, binomial),
+ test="Chisq")
Analysis of Deviance Table
Model: binomial, link: logit
Response: counts/total
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 5 136.910
age 2 134.857 3 2.054 <2e-16 ***
type 1 1.273 2 0.780 0.2591
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> juul.girl <- transform(subset(juul,age>8 & age<20 &
+ complete.cases(menarche)),
+ menarche=factor(menarche))
> logit.menarche <- glm(menarche~age+I(age^2)+I(age^3),
+ binomial, data=juul.girl)
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
> probit.menarche <- glm(menarche~age+I(age^2)+I(age^3),
+ binomial(probit), data=juul.girl)
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
> summary(logit.menarche)
Call:
glm(formula = menarche ~ age + I(age^2) + I(age^3), family = binomial,
data = juul.girl)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -766.0804 377.7631 -2.028 0.0426 *
age 175.2400 87.1896 2.010 0.0444 *
I(age^2) -13.4140 6.6902 -2.005 0.0450 *
I(age^3) 0.3435 0.1707 2.013 0.0442 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 719.39 on 518 degrees of freedom
Residual deviance: 191.80 on 515 degrees of freedom
AIC: 199.8
Number of Fisher Scoring iterations: 12
> summary(probit.menarche)
Call:
glm(formula = menarche ~ age + I(age^2) + I(age^3), family = binomial(probit),
data = juul.girl)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -328.53729 184.42237 -1.781 0.0748 .
age 74.94081 42.66126 1.757 0.0790 .
I(age^2) -5.73565 3.27880 -1.749 0.0802 .
I(age^3) 0.14722 0.08373 1.758 0.0787 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 719.39 on 518 degrees of freedom
Residual deviance: 191.98 on 515 degrees of freedom
AIC: 199.98
Number of Fisher Scoring iterations: 13
> Age=seq(8,20,.1)
> newages <- data.frame(age=Age)
> p.logit <- predict(logit.menarche,newdata=newages,type="resp")
> p.probit <- predict(probit.menarche,newdata=newages,type="resp")
> matplot(Age,cbind(p.probit,p.logit),type="l")
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> library(survival)
Attaching package: 'survival'
The following object is masked from 'package:ISwR':
lung
> attach(graft.vs.host)
> plot(survfit(Surv(time,dead)~gvhd))
> survdiff(Surv(time,dead)~gvhd)
Call:
survdiff(formula = Surv(time, dead) ~ gvhd)
N Observed Expected (O-E)^2/E (O-E)^2/V
gvhd=0 20 7 11.99 2.07 6.55
gvhd=1 17 11 6.01 4.14 6.55
Chisq= 6.6 on 1 degrees of freedom, p= 0.01
> summary(coxph(Surv(time,dead) ~ gvhd)) # for comparison
Call:
coxph(formula = Surv(time, dead) ~ gvhd)
n= 37, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
gvhd 1.2419 3.4620 0.5144 2.414 0.0158 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
gvhd 3.462 0.2888 1.263 9.489
Concordance= 0.671 (se = 0.052 )
Likelihood ratio test= 6.19 on 1 df, p=0.01
Wald test = 5.83 on 1 df, p=0.02
Score (logrank) test = 6.55 on 1 df, p=0.01
> summary(coxph(Surv(time,dead) ~
+ gvhd + log(index) + donage + rcpage + preg))
Call:
coxph(formula = Surv(time, dead) ~ gvhd + log(index) + donage +
rcpage + preg)
n= 37, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
gvhd 1.29074 3.63548 0.69131 1.867 0.0619 .
log(index) -0.49623 0.60882 0.35579 -1.395 0.1631
donage 0.03440 1.03500 0.04000 0.860 0.3899
rcpage -0.01230 0.98778 0.04085 -0.301 0.7634
preg 1.13641 3.11555 0.57058 1.992 0.0464 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
gvhd 3.6355 0.2751 0.9378 14.093
log(index) 0.6088 1.6425 0.3031 1.223
donage 1.0350 0.9662 0.9569 1.119
rcpage 0.9878 1.0124 0.9118 1.070
preg 3.1155 0.3210 1.0183 9.533
Concordance= 0.748 (se = 0.065 )
Likelihood ratio test= 14.33 on 5 df, p=0.01
Wald test = 14.86 on 5 df, p=0.01
Score (logrank) test = 17.48 on 5 df, p=0.004
> attach(melanom)
> cox1 <- coxph(Surv(days, status==1) ~
+ log(thick) + sex + strata(ulc))
> new <- data.frame(sex=2, thick=c(0.1, 0.2, 0.5))
> svfit <- survfit(cox1,newdata=new)
Error in a$specials[[i]] : subscript out of bounds
Calls: survfit ... survfit.coxph -> model.matrix.coxph -> delete.response -> off
Execution halted
Running the tests in ‘tests/allscripts.R’ failed.
Complete output:
> library(ISwR)
> .make.epsf <- Sys.getenv("EPSF")=="y"
> ps.options(height=3.5, width=4.4, pointsize=8, horiz=F)
> if (.make.epsf) X11(height=3.5,width=4.4,pointsize=8) else postscript()
> dev.copy2eps <- function(...) invisible(grDevices::dev.copy2eps(...))
> par(mar=c(4,4,3,2)+.1)
> options(width=66, useFancyQuotes="TeX")
> suppressWarnings(RNGversion("1.5.1")) #Yes, Kinderman-Ramage was buggy...
> set.seed(310367)
> #Rprof(interval=.001)
> plot(rnorm(500))
> 2 + 2
[1] 4
> exp(-2)
[1] 0.1353353
> rnorm(15)
[1] -0.18326112 -0.59753287 -0.67017905 0.16075723 1.28199575
[6] 0.07976977 0.13683303 0.77155246 0.85986694 -1.01506772
[11] -0.49448567 0.52433026 1.07732656 1.09748097 -1.09318582
> x <- 2
> x
[1] 2
> x + x
[1] 4
> weight <- c(60, 72, 57, 90, 95, 72)
> weight
[1] 60 72 57 90 95 72
> height <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91)
> bmi <- weight/height^2
> bmi
[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630
> sum(weight)
[1] 446
> sum(weight)/length(weight)
[1] 74.33333
> xbar <- sum(weight)/length(weight)
> weight - xbar
[1] -14.333333 -2.333333 -17.333333 15.666667 20.666667
[6] -2.333333
> (weight - xbar)^2
[1] 205.444444 5.444444 300.444444 245.444444 427.111111
[6] 5.444444
> sum((weight - xbar)^2)
[1] 1189.333
> sqrt(sum((weight - xbar)^2)/(length(weight) - 1))
[1] 15.42293
> mean(weight)
[1] 74.33333
> sd(weight)
[1] 15.42293
> t.test(bmi, mu=22.5)
One Sample t-test
data: bmi
t = 0.34488, df = 5, p-value = 0.7442
alternative hypothesis: true mean is not equal to 22.5
95 percent confidence interval:
18.41734 27.84791
sample estimates:
mean of x
23.13262
> plot(height,weight)
> if (.make.epsf) dev.copy2eps(file="h-w.ps")
> plot(height, weight, pch=2)
> if (.make.epsf) dev.copy2eps(file="h-w-triangle.ps")
> hh <- c(1.65, 1.70, 1.75, 1.80, 1.85, 1.90)
> lines(hh, 22.5 * hh^2)
> if (.make.epsf) dev.copy2eps(file="h-w-line.ps")
> # args(plot.default) # disabled because unimportant and changed btw 4.5 & 4.6
> c("Huey","Dewey","Louie")
[1] "Huey" "Dewey" "Louie"
> c('Huey','Dewey','Louie')
[1] "Huey" "Dewey" "Louie"
> c(T,T,F,T)
[1] TRUE TRUE FALSE TRUE
> bmi > 25
[1] FALSE FALSE FALSE FALSE TRUE FALSE
> cat(c("Huey","Dewey","Louie"))
Huey Dewey Louie> cat("Huey","Dewey","Louie", "\n")
Huey Dewey Louie
> cat("What is \"R\"?\n")
What is "R"?
> c(42,57,12,39,1,3,4)
[1] 42 57 12 39 1 3 4
> x <- c(1, 2, 3)
> y <- c(10, 20)
> c(x, y, 5)
[1] 1 2 3 10 20 5
> x <- c(red="Huey", blue="Dewey", green="Louie")
> x
red blue green
"Huey" "Dewey" "Louie"
> names(x)
[1] "red" "blue" "green"
> c(FALSE, 3)
[1] 0 3
> c(pi, "abc")
[1] "3.14159265358979" "abc"
> c(FALSE, "abc")
[1] "FALSE" "abc"
> seq(4,9)
[1] 4 5 6 7 8 9
> seq(4,10,2)
[1] 4 6 8 10
> 4:9
[1] 4 5 6 7 8 9
> oops <- c(7,9,13)
> rep(oops,3)
[1] 7 9 13 7 9 13 7 9 13
> rep(oops,1:3)
[1] 7 9 9 13 13 13
> rep(1:2,c(10,15))
[1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
> x <- 1:12
> dim(x) <- c(3,4)
> x
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
> matrix(1:12,nrow=3,byrow=T)
[,1] [,2] [,3] [,4]
[1,] 1 2 3 4
[2,] 5 6 7 8
[3,] 9 10 11 12
> x <- matrix(1:12,nrow=3,byrow=T)
> rownames(x) <- LETTERS[1:3]
> x
[,1] [,2] [,3] [,4]
A 1 2 3 4
B 5 6 7 8
C 9 10 11 12
> t(x)
A B C
[1,] 1 5 9
[2,] 2 6 10
[3,] 3 7 11
[4,] 4 8 12
> cbind(A=1:4,B=5:8,C=9:12)
A B C
[1,] 1 5 9
[2,] 2 6 10
[3,] 3 7 11
[4,] 4 8 12
> rbind(A=1:4,B=5:8,C=9:12)
[,1] [,2] [,3] [,4]
A 1 2 3 4
B 5 6 7 8
C 9 10 11 12
> pain <- c(0,3,2,2,1)
> fpain <- factor(pain,levels=0:3)
> levels(fpain) <- c("none","mild","medium","severe")
> fpain
[1] none severe medium medium mild
Levels: none mild medium severe
> as.numeric(fpain)
[1] 1 4 3 3 2
> levels(fpain)
[1] "none" "mild" "medium" "severe"
> intake.pre <- c(5260,5470,5640,6180,6390,
+ 6515,6805,7515,7515,8230,8770)
> intake.post <- c(3910,4220,3885,5160,5645,
+ 4680,5265,5975,6790,6900,7335)
> mylist <- list(before=intake.pre,after=intake.post)
> mylist
$before
[1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770
$after
[1] 3910 4220 3885 5160 5645 4680 5265 5975 6790 6900 7335
> mylist$before
[1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770
> d <- data.frame(intake.pre,intake.post)
> d
intake.pre intake.post
1 5260 3910
2 5470 4220
3 5640 3885
4 6180 5160
5 6390 5645
6 6515 4680
7 6805 5265
8 7515 5975
9 7515 6790
10 8230 6900
11 8770 7335
> d$intake.pre
[1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770
> intake.pre[5]
[1] 6390
> intake.pre[c(3,5,7)]
[1] 5640 6390 6805
> v <- c(3,5,7)
> intake.pre[v]
[1] 5640 6390 6805
> intake.pre[1:5]
[1] 5260 5470 5640 6180 6390
> intake.pre[-c(3,5,7)]
[1] 5260 5470 6180 6515 7515 7515 8230 8770
> intake.post[intake.pre > 7000]
[1] 5975 6790 6900 7335
> intake.post[intake.pre > 7000 & intake.pre <= 8000]
[1] 5975 6790
> intake.pre > 7000 & intake.pre <= 8000
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
[11] FALSE
> d <- data.frame(intake.pre,intake.post)
> d[5,1]
[1] 6390
> d[5,]
intake.pre intake.post
5 6390 5645
> d[d$intake.pre>7000,]
intake.pre intake.post
8 7515 5975
9 7515 6790
10 8230 6900
11 8770 7335
> sel <- d$intake.pre>7000
> sel
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[11] TRUE
> d[sel,]
intake.pre intake.post
8 7515 5975
9 7515 6790
10 8230 6900
11 8770 7335
> d[1:2,]
intake.pre intake.post
1 5260 3910
2 5470 4220
> head(d)
intake.pre intake.post
1 5260 3910
2 5470 4220
3 5640 3885
4 6180 5160
5 6390 5645
6 6515 4680
> energy
expend stature
1 9.21 obese
2 7.53 lean
3 7.48 lean
4 8.08 lean
5 8.09 lean
6 10.15 lean
7 8.40 lean
8 10.88 lean
9 6.13 lean
10 7.90 lean
11 11.51 obese
12 12.79 obese
13 7.05 lean
14 11.85 obese
15 9.97 obese
16 7.48 lean
17 8.79 obese
18 9.69 obese
19 9.68 obese
20 7.58 lean
21 9.19 obese
22 8.11 lean
> exp.lean <- energy$expend[energy$stature=="lean"]
> exp.obese <- energy$expend[energy$stature=="obese"]
> l <- split(energy$expend, energy$stature)
> l
$lean
[1] 7.53 7.48 8.08 8.09 10.15 8.40 10.88 6.13 7.90 7.05
[11] 7.48 7.58 8.11
$obese
[1] 9.21 11.51 12.79 11.85 9.97 8.79 9.69 9.68 9.19
> lapply(thuesen, mean, na.rm=T)
$blood.glucose
[1] 10.3
$short.velocity
[1] 1.325652
> sapply(thuesen, mean, na.rm=T)
blood.glucose short.velocity
10.300000 1.325652
> replicate(10,mean(rexp(20)))
[1] 1.0677019 1.2166898 0.8923216 1.1281207 0.9636017 0.8406877
[7] 1.3357814 0.8249408 0.9488707 0.5724575
> m <- matrix(rnorm(12),4)
> m
[,1] [,2] [,3]
[1,] -2.5710730 0.2524470 -0.16886795
[2,] 0.5509498 1.5430648 0.05359794
[3,] 2.4002722 0.1624704 -1.23407417
[4,] 1.4791103 0.9484525 -0.84670929
> apply(m, 2, min)
[1] -2.5710730 0.1624704 -1.2340742
> tapply(energy$expend, energy$stature, median)
lean obese
7.90 9.69
> intake$post
[1] 3910 4220 3885 5160 5645 4680 5265 5975 6790 6900 7335
> sort(intake$post)
[1] 3885 3910 4220 4680 5160 5265 5645 5975 6790 6900 7335
> order(intake$post)
[1] 3 1 2 6 4 7 5 8 9 10 11
> o <- order(intake$post)
> intake$post[o]
[1] 3885 3910 4220 4680 5160 5265 5645 5975 6790 6900 7335
> intake$pre[o]
[1] 5640 5260 5470 6515 6180 6805 6390 7515 7515 8230 8770
> intake.sorted <- intake[o,]
> save.image("ch1.RData")
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> load("ch1.RData")
> .foo <- dev.copy2eps
> rm(dev.copy2eps)
> ls()
[1] "bmi" "d" "exp.lean"
[4] "exp.obese" "fpain" "height"
[7] "hh" "intake.post" "intake.pre"
[10] "intake.sorted" "l" "m"
[13] "mylist" "o" "oops"
[16] "pain" "sel" "v"
[19] "weight" "x" "xbar"
[22] "y"
> dev.copy2eps <- .foo
> rm(height, weight)
> sink("myfile")
> ls()
> sink()
> attach(thuesen)
> blood.glucose
[1] 15.3 10.8 8.1 19.5 7.2 5.3 9.3 11.1 7.5 12.2 6.7 5.2
[13] 19.0 15.1 6.7 8.6 4.2 10.3 12.5 16.1 13.3 4.9 8.8 9.5
> search()
[1] ".GlobalEnv" "thuesen" "package:ISwR"
[4] "package:stats" "package:graphics" "package:grDevices"
[7] "package:utils" "package:datasets" "package:methods"
[10] "Autoloads" "package:base"
> detach()
> search()
[1] ".GlobalEnv" "package:ISwR" "package:stats"
[4] "package:graphics" "package:grDevices" "package:utils"
[7] "package:datasets" "package:methods" "Autoloads"
[10] "package:base"
> thue2 <- subset(thuesen,blood.glucose<7)
> thue2
blood.glucose short.velocity
6 5.3 1.49
11 6.7 1.25
12 5.2 1.19
15 6.7 1.52
17 4.2 1.12
22 4.9 1.03
> thue3 <- transform(thuesen,log.gluc=log(blood.glucose))
> thue3
blood.glucose short.velocity log.gluc
1 15.3 1.76 2.727853
2 10.8 1.34 2.379546
3 8.1 1.27 2.091864
4 19.5 1.47 2.970414
5 7.2 1.27 1.974081
6 5.3 1.49 1.667707
7 9.3 1.31 2.230014
8 11.1 1.09 2.406945
9 7.5 1.18 2.014903
10 12.2 1.22 2.501436
11 6.7 1.25 1.902108
12 5.2 1.19 1.648659
13 19.0 1.95 2.944439
14 15.1 1.28 2.714695
15 6.7 1.52 1.902108
16 8.6 NA 2.151762
17 4.2 1.12 1.435085
18 10.3 1.37 2.332144
19 12.5 1.19 2.525729
20 16.1 1.05 2.778819
21 13.3 1.32 2.587764
22 4.9 1.03 1.589235
23 8.8 1.12 2.174752
24 9.5 1.70 2.251292
> thue4 <- within(thuesen,{
+ log.gluc <- log(blood.glucose)
+ m <- mean(log.gluc)
+ centered.log.gluc <- log.gluc - m
+ rm(m)
+ })
> thue4
blood.glucose short.velocity centered.log.gluc log.gluc
1 15.3 1.76 0.481879807 2.727853
2 10.8 1.34 0.133573113 2.379546
3 8.1 1.27 -0.154108960 2.091864
4 19.5 1.47 0.724441444 2.970414
5 7.2 1.27 -0.271891996 1.974081
6 5.3 1.49 -0.578266201 1.667707
7 9.3 1.31 -0.015958621 2.230014
8 11.1 1.09 0.160972087 2.406945
9 7.5 1.18 -0.231070001 2.014903
10 12.2 1.22 0.255462930 2.501436
11 6.7 1.25 -0.343865495 1.902108
12 5.2 1.19 -0.597314396 1.648659
13 19.0 1.95 0.698465958 2.944439
14 15.1 1.28 0.468721722 2.714695
15 6.7 1.52 -0.343865495 1.902108
16 8.6 NA -0.094210818 2.151762
17 4.2 1.12 -0.810888496 1.435085
18 10.3 1.37 0.086170874 2.332144
19 12.5 1.19 0.279755623 2.525729
20 16.1 1.05 0.532846250 2.778819
21 13.3 1.32 0.341791014 2.587764
22 4.9 1.03 -0.656737817 1.589235
23 8.8 1.12 -0.071221300 2.174752
24 9.5 1.70 0.005318777 2.251292
> d <- par(mar=c(5,4,4,2)+.1)
> x <- runif(50,0,2)
> y <- runif(50,0,2)
> plot(x, y, main="Main title", sub="subtitle",
+ xlab="x-label", ylab="y-label")
> text(0.6,0.6,"text at (0.6,0.6)")
> abline(h=.6,v=.6)
> for (side in 1:4) mtext(-1:4,side=side,at=.7,line=-1:4)
> mtext(paste("side",1:4), side=1:4, line=-1,font=2)
> if (.make.epsf) dev.copy2eps(file="layout.ps")
> par(d)
> plot(x, y, type="n", xlab="", ylab="", axes=F)
> points(x,y)
> axis(1)
> axis(2,at=seq(0.2,1.8,0.2))
> box()
> title(main="Main title", sub="subtitle",
+ xlab="x-label", ylab="y-label")
> set.seed(1234) #make it happen....
> x <- rnorm(100)
> hist(x,freq=F)
> curve(dnorm(x),add=T)
> h <- hist(x, plot=F)
> ylim <- range(0, h$density, dnorm(0))
> hist(x, freq=F, ylim=ylim)
> curve(dnorm(x), add=T)
> if (.make.epsf) dev.copy2eps(file="hist+norm.ps")
> hist.with.normal <- function(x, xlab=deparse(substitute(x)),...)
+ {
+ h <- hist(x, plot=F, ...)
+ s <- sd(x)
+ m <- mean(x)
+ ylim <- range(0,h$density,dnorm(0,sd=s))
+ hist(x, freq=F, ylim=ylim, xlab=xlab, ...)
+ curve(dnorm(x,m,s), add=T)
+ }
> hist.with.normal(rnorm(200))
> y <- 12345
> x <- y/2
> while (abs(x*x-y) > 1e-10) x <- (x + y/x)/2
> x
[1] 111.1081
> x^2
[1] 12345
> x <- y/2
> repeat{
+ x <- (x + y/x)/2
+ if (abs(x*x-y) < 1e-10) break
+ }
> x
[1] 111.1081
> x <- seq(0, 1,.05)
> plot(x, x, ylab="y", type="l")
> for ( j in 2:8 ) lines(x, x^j)
> t.test(bmi, mu=22.5)$p.value
[1] 0.7442183
> print
function (x, ...)
UseMethod("print")
<bytecode: 0x559a8deb5e40>
<environment: namespace:base>
> ## length(methods("print")) # quoted in text
> ## (this test zapped 2024-10-01 due to platform dependency)
> thuesen2 <- read.table(
+ system.file("rawdata","thuesen.txt",package="ISwR"), header=T)
> thuesen2
blood.glucose short.velocity
1 15.3 1.76
2 10.8 1.34
3 8.1 1.27
4 19.5 1.47
5 7.2 1.27
6 5.3 1.49
7 9.3 1.31
8 11.1 1.09
9 7.5 1.18
10 12.2 1.22
11 6.7 1.25
12 5.2 1.19
13 19.0 1.95
14 15.1 1.28
15 6.7 1.52
16 8.6 NA
17 4.2 1.12
18 10.3 1.37
19 12.5 1.19
20 16.1 1.05
21 13.3 1.32
22 4.9 1.03
23 8.8 1.12
24 9.5 1.70
> levels(secretin$time)
[1] "20" "30" "60" "90" "pre"
> ## IGNORE_RDIFF_BEGIN
> # keep CRAN happy - this output is obviously system-dependent
> system.file("rawdata", "thuesen.txt", package="ISwR")
[1] "/home/hornik/tmp/R.check/r-devel-gcc/Work/build/Packages/ISwR/rawdata/thuesen.txt"
> ## IGNORE_RDIFF_END
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> sample(1:40,5)
[1] 19 8 33 21 31
> sample(c("H","T"), 10, replace=T)
[1] "T" "T" "H" "T" "T" "T" "H" "T" "H" "H"
> sample(c("succ", "fail"), 10, replace=T, prob=c(0.9, 0.1))
[1] "fail" "fail" "succ" "succ" "fail" "succ" "succ" "succ"
[9] "succ" "succ"
> 1/prod(40:36)
[1] 1.266449e-08
> prod(5:1)/prod(40:36)
[1] 1.519738e-06
> 1/choose(40,5)
[1] 1.519738e-06
> x <- seq(-4,4,0.1)
> plot(x,dnorm(x),type="l")
> if (.make.epsf) dev.copy2eps(file="bellcurve.ps")
> x <- 0:50
> plot(x,dbinom(x,size=50,prob=.33),type="h")
> if (.make.epsf) dev.copy2eps(file="binomdist.ps")
> 1-pnorm(160,mean=132,sd=13)
[1] 0.01562612
> pbinom(16,size=20,prob=.5)
[1] 0.9987116
> 1-pbinom(15,size=20,prob=.5)
[1] 0.005908966
> 1-pbinom(15,20,.5)+pbinom(4,20,.5)
[1] 0.01181793
> xbar <- 83
> sigma <- 12
> n <- 5
> sem <- sigma/sqrt(n)
> sem
[1] 5.366563
> xbar + sem * qnorm(0.025)
[1] 72.48173
> xbar + sem * qnorm(0.975)
[1] 93.51827
> set.seed(310367)
> rnorm(10)
[1] -0.2996466 -0.1718510 -0.1955634 1.2280843 -2.6074190
[6] -0.2999453 -0.4655102 -1.5680666 1.2545876 -1.8028839
> rnorm(10)
[1] 1.7082495 0.1432875 -1.0271750 -0.9246647 0.6402383
[6] 0.7201677 -0.3071239 1.2090712 0.8699669 0.5882753
> rnorm(10,mean=7,sd=5)
[1] 8.934983 8.611855 4.675578 3.670129 4.223117 5.484290
[7] 12.141946 8.057541 -2.893164 13.590586
> rbinom(10,size=20,prob=.5)
[1] 12 11 10 8 11 8 11 8 8 13
> ## no data sets used by exercises
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> x <- rnorm(50)
> mean(x)
[1] -0.1565061
> sd(x)
[1] 1.175373
> var(x)
[1] 1.381502
> median(x)
[1] -0.3560654
> quantile(x)
0% 25% 50% 75% 100%
-2.1565603 -0.9689894 -0.3560654 0.6133640 2.9887241
> pvec <- seq(0,1,0.1)
> pvec
[1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
> quantile(x,pvec)
0% 10% 20% 30% 40%
-2.15656030 -1.54828008 -1.13398001 -0.79701920 -0.62973546
50% 60% 70% 80% 90%
-0.35606539 -0.05965905 0.36950570 0.85233024 1.29837806
100%
2.98872414
> attach(juul)
> mean(igf1)
[1] NA
> mean(igf1,na.rm=T)
[1] 340.168
> sum(!is.na(igf1))
[1] 1018
> summary(igf1)
Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
25.0 202.2 313.5 340.2 462.8 915.0 321
> summary(juul)
age menarche sex igf1
Min. : 0.170 Min. :1.000 Min. :1.000 Min. : 25.0
1st Qu.: 9.053 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:202.2
Median :12.560 Median :1.000 Median :2.000 Median :313.5
Mean :15.095 Mean :1.476 Mean :1.534 Mean :340.2
3rd Qu.:16.855 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:462.8
Max. :83.000 Max. :2.000 Max. :2.000 Max. :915.0
NAs :5 NAs :635 NAs :5 NAs :321
tanner testvol
Min. :1.00 Min. : 1.000
1st Qu.:1.00 1st Qu.: 1.000
Median :2.00 Median : 3.000
Mean :2.64 Mean : 7.896
3rd Qu.:5.00 3rd Qu.:15.000
Max. :5.00 Max. :30.000
NAs :240 NAs :859
> detach(juul)
> juul$sex <- factor(juul$sex,labels=c("M","F"))
> juul$menarche <- factor(juul$menarche,labels=c("No","Yes"))
> juul$tanner <- factor(juul$tanner,
+ labels=c("I","II","III","IV","V"))
> attach(juul)
> summary(juul)
age menarche sex igf1 tanner
Min. : 0.170 No :369 M :621 Min. : 25.0 I :515
1st Qu.: 9.053 Yes:335 F :713 1st Qu.:202.2 II :103
Median :12.560 NAs:635 NAs: 5 Median :313.5 III: 72
Mean :15.095 Mean :340.2 IV : 81
3rd Qu.:16.855 3rd Qu.:462.8 V :328
Max. :83.000 Max. :915.0 NAs:240
NAs :5 NAs :321
testvol
Min. : 1.000
1st Qu.: 1.000
Median : 3.000
Mean : 7.896
3rd Qu.:15.000
Max. :30.000
NAs :859
> hist(x)
> if (.make.epsf) dev.copy2eps(file="hist.ps")
> mid.age <- c(2.5,7.5,13,16.5,17.5,19,22.5,44.5,70.5)
> acc.count <- c(28,46,58,20,31,64,149,316,103)
> age.acc <- rep(mid.age,acc.count)
> brk <- c(0,5,10,16,17,18,20,25,60,80)
> hist(age.acc,breaks=brk)
> if (.make.epsf) dev.copy2eps(file="hist-acc-right.ps")
> n <- length(x)
> plot(sort(x),(1:n)/n,type="s",ylim=c(0,1))
> if (.make.epsf) dev.copy2eps(file="empdist.ps")
> qqnorm(x)
> if (.make.epsf) dev.copy2eps(file="qqnorm.ps")
> par(mfrow=c(1,2))
> boxplot(IgM)
> boxplot(log(IgM))
> par(mfrow=c(1,1))
> if (.make.epsf) dev.copy2eps(file="boxplot-IgM.ps")
> attach(red.cell.folate)
> tapply(folate,ventilation,mean)
N2O+O2,24h N2O+O2,op O2,24h
316.6250 256.4444 278.0000
> tapply(folate,ventilation,sd)
N2O+O2,24h N2O+O2,op O2,24h
58.71709 37.12180 33.75648
> tapply(folate,ventilation,length)
N2O+O2,24h N2O+O2,op O2,24h
8 9 5
> xbar <- tapply(folate, ventilation, mean)
> s <- tapply(folate, ventilation, sd)
> n <- tapply(folate, ventilation, length)
> cbind(mean=xbar, std.dev=s, n=n)
mean std.dev n
N2O+O2,24h 316.6250 58.71709 8
N2O+O2,op 256.4444 37.12180 9
O2,24h 278.0000 33.75648 5
> tapply(igf1, tanner, mean)
I II III IV V
NA NA NA NA NA
> tapply(igf1, tanner, mean, na.rm=T)
I II III IV V
207.4727 352.6714 483.2222 513.0172 465.3344
> aggregate(juul[c("age","igf1")],
+ list(sex=juul$sex), mean, na.rm=T)
sex age igf1
1 M 15.38436 310.8866
2 F 14.84363 368.1006
> aggregate(juul[c("age","igf1")], juul["sex"], mean, na.rm=T)
sex age igf1
1 M 15.38436 310.8866
2 F 14.84363 368.1006
> by(juul, juul["sex"], summary)
sex: M
age menarche sex igf1 tanner
Min. : 0.17 No : 0 M:621 Min. : 29.0 I :291
1st Qu.: 8.85 Yes: 0 F: 0 1st Qu.:176.0 II : 55
Median :12.38 NAs:621 Median :280.0 III: 34
Mean :15.38 Mean :310.9 IV : 41
3rd Qu.:16.77 3rd Qu.:430.2 V :124
Max. :83.00 Max. :915.0 NAs: 76
NAs :145
testvol
Min. : 1.000
1st Qu.: 1.000
Median : 3.000
Mean : 7.896
3rd Qu.:15.000
Max. :30.000
NAs :141
-------------------------------------------------
sex: F
age menarche sex igf1 tanner
Min. : 0.25 No :369 M: 0 Min. : 25.0 I :224
1st Qu.: 9.30 Yes:335 F:713 1st Qu.:233.0 II : 48
Median :12.80 NAs: 9 Median :352.0 III: 38
Mean :14.84 Mean :368.1 IV : 40
3rd Qu.:16.93 3rd Qu.:483.0 V :204
Max. :75.12 Max. :914.0 NAs:159
NAs :176
testvol
Min. : NA
1st Qu.: NA
Median : NA
Mean :NaN
3rd Qu.: NA
Max. : NA
NAs :713
> attach(energy)
> expend.lean <- expend[stature=="lean"]
> expend.obese <- expend[stature=="obese"]
> par(mfrow=c(2,1))
> hist(expend.lean,breaks=10,xlim=c(5,13),ylim=c(0,4),col="white")
> hist(expend.obese,breaks=10,xlim=c(5,13),ylim=c(0,4),col="grey")
> par(mfrow=c(1,1))
> if (.make.epsf) dev.copy2eps(file="expend-hist-2on1.ps")
> boxplot(expend ~ stature)
> if (.make.epsf) dev.copy2eps(file="boxplots-expend-stat.ps")
> boxplot(expend.lean,expend.obese)
> opar <- par(mfrow=c(2,2), mex=0.8, mar=c(3,3,2,1)+.1)
> stripchart(expend ~ stature)
> stripchart(expend ~ stature, method="stack")
> stripchart(expend ~ stature, method="jitter")
> stripchart(expend ~ stature, method="jitter", jitter=.03)
> par(opar)
> if (.make.epsf) dev.copy2eps(file="stripcharts-expend-stat.ps")
> caff.marital <- matrix(c(652,1537,598,242,36,46,38,21,218
+ ,327,106,67),
+ nrow=3,byrow=T)
> caff.marital
[,1] [,2] [,3] [,4]
[1,] 652 1537 598 242
[2,] 36 46 38 21
[3,] 218 327 106 67
> colnames(caff.marital) <- c("0","1-150","151-300",">300")
> rownames(caff.marital) <- c("Married","Prev.married","Single")
> caff.marital
0 1-150 151-300 >300
Married 652 1537 598 242
Prev.married 36 46 38 21
Single 218 327 106 67
> names(dimnames(caff.marital)) <- c("marital","consumption")
> caff.marital
consumption
marital 0 1-150 151-300 >300
Married 652 1537 598 242
Prev.married 36 46 38 21
Single 218 327 106 67
> as.data.frame(as.table(caff.marital))
marital consumption Freq
1 Married 0 652
2 Prev.married 0 36
3 Single 0 218
4 Married 1-150 1537
5 Prev.married 1-150 46
6 Single 1-150 327
7 Married 151-300 598
8 Prev.married 151-300 38
9 Single 151-300 106
10 Married >300 242
11 Prev.married >300 21
12 Single >300 67
> table(sex)
sex
M F
621 713
> table(sex,menarche)
menarche
sex No Yes
M 0 0
F 369 335
> table(menarche,tanner)
tanner
menarche I II III IV V
No 221 43 32 14 2
Yes 1 1 5 26 202
> xtabs(~ tanner + sex, data=juul)
sex
tanner M F
I 291 224
II 55 48
III 34 38
IV 41 40
V 124 204
> xtabs(~ dgn + diab + coma, data=stroke)
, , coma = No
diab
dgn No Yes
ICH 53 6
ID 143 21
INF 411 64
SAH 38 0
, , coma = Yes
diab
dgn No Yes
ICH 19 1
ID 23 3
INF 23 2
SAH 9 0
> ftable(coma + diab ~ dgn, data=stroke)
coma No Yes
diab No Yes No Yes
dgn
ICH 53 6 19 1
ID 143 21 23 3
INF 411 64 23 2
SAH 38 0 9 0
> t(caff.marital)
marital
consumption Married Prev.married Single
0 652 36 218
1-150 1537 46 327
151-300 598 38 106
>300 242 21 67
> tanner.sex <- table(tanner,sex)
> tanner.sex
sex
tanner M F
I 291 224
II 55 48
III 34 38
IV 41 40
V 124 204
> margin.table(tanner.sex,1)
tanner
I II III IV V
515 103 72 81 328
> margin.table(tanner.sex,2)
sex
M F
545 554
> prop.table(tanner.sex,1)
sex
tanner M F
I 0.5650485 0.4349515
II 0.5339806 0.4660194
III 0.4722222 0.5277778
IV 0.5061728 0.4938272
V 0.3780488 0.6219512
> tanner.sex/sum(tanner.sex)
sex
tanner M F
I 0.26478617 0.20382166
II 0.05004550 0.04367607
III 0.03093722 0.03457689
IV 0.03730664 0.03639672
V 0.11282985 0.18562329
> total.caff <- margin.table(caff.marital,2)
> total.caff
consumption
0 1-150 151-300 >300
906 1910 742 330
> barplot(total.caff, col="white")
> if (.make.epsf) dev.copy2eps(file="simple-bar.ps")
> par(mfrow=c(2,2))
> barplot(caff.marital, col="white")
> barplot(t(caff.marital), col="white")
> barplot(t(caff.marital), col="white", beside=T)
> barplot(prop.table(t(caff.marital),2), col="white", beside=T)
> par(mfrow=c(1,1))
> if (.make.epsf) dev.copy2eps(file="mat-4-bar.ps")
> barplot(prop.table(t(caff.marital),2),beside=T,
+ legend.text=colnames(caff.marital),
+ col=c("white","grey80","grey50","black"))
> if (.make.epsf) dev.copy2eps(file="pretty-bar.ps")
> dotchart(t(caff.marital), lcolor="black")
> if (.make.epsf) dev.copy2eps(file="dotchart.ps")
> opar <- par(mfrow=c(2,2),mex=0.8, mar=c(1,1,2,1))
> slices <- c("white","grey80","grey50","black")
> pie(caff.marital["Married",], main="Married", col=slices)
> pie(caff.marital["Prev.married",],
+ main="Previously married", col=slices)
> pie(caff.marital["Single",], main="Single", col=slices)
> par(opar)
> if (.make.epsf) dev.copy2eps(file="pie.ps")
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> daily.intake <- c(5260,5470,5640,6180,6390,6515,
+ 6805,7515,7515,8230,8770)
> mean(daily.intake)
[1] 6753.636
> sd(daily.intake)
[1] 1142.123
> quantile(daily.intake)
0% 25% 50% 75% 100%
5260 5910 6515 7515 8770
> t.test(daily.intake,mu=7725)
One Sample t-test
data: daily.intake
t = -2.8208, df = 10, p-value = 0.01814
alternative hypothesis: true mean is not equal to 7725
95 percent confidence interval:
5986.348 7520.925
sample estimates:
mean of x
6753.636
> t.test(daily.intake,mu=7725)
One Sample t-test
data: daily.intake
t = -2.8208, df = 10, p-value = 0.01814
alternative hypothesis: true mean is not equal to 7725
95 percent confidence interval:
5986.348 7520.925
sample estimates:
mean of x
6753.636
> wilcox.test(daily.intake, mu=7725, exact=FALSE)
Wilcoxon signed rank test with continuity correction
data: daily.intake
V = 8, p-value = 0.0293
alternative hypothesis: true location is not equal to 7725
> attach(energy)
> energy
expend stature
1 9.21 obese
2 7.53 lean
3 7.48 lean
4 8.08 lean
5 8.09 lean
6 10.15 lean
7 8.40 lean
8 10.88 lean
9 6.13 lean
10 7.90 lean
11 11.51 obese
12 12.79 obese
13 7.05 lean
14 11.85 obese
15 9.97 obese
16 7.48 lean
17 8.79 obese
18 9.69 obese
19 9.68 obese
20 7.58 lean
21 9.19 obese
22 8.11 lean
> t.test(expend~stature)
Welch Two Sample t-test
data: expend by stature
t = -3.8555, df = 15.919, p-value = 0.001411
alternative hypothesis: true difference in means between group lean and group obese is not equal to 0
95 percent confidence interval:
-3.459167 -1.004081
sample estimates:
mean in group lean mean in group obese
8.066154 10.297778
> t.test(expend~stature, var.equal=T)
Two Sample t-test
data: expend by stature
t = -3.9456, df = 20, p-value = 0.000799
alternative hypothesis: true difference in means between group lean and group obese is not equal to 0
95 percent confidence interval:
-3.411451 -1.051796
sample estimates:
mean in group lean mean in group obese
8.066154 10.297778
> var.test(expend~stature)
F test to compare two variances
data: expend by stature
F = 0.78445, num df = 12, denom df = 8, p-value = 0.6797
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.1867876 2.7547991
sample estimates:
ratio of variances
0.784446
> wilcox.test(expend~stature, exact=FALSE)
Wilcoxon rank sum test with continuity correction
data: expend by stature
W = 12, p-value = 0.002122
alternative hypothesis: true location shift is not equal to 0
> attach(intake)
> intake
pre post
1 5260 3910
2 5470 4220
3 5640 3885
4 6180 5160
5 6390 5645
6 6515 4680
7 6805 5265
8 7515 5975
9 7515 6790
10 8230 6900
11 8770 7335
> post - pre
[1] -1350 -1250 -1755 -1020 -745 -1835 -1540 -1540 -725 -1330
[11] -1435
> t.test(pre, post, paired=T)
Paired t-test
data: pre and post
t = 11.941, df = 10, p-value = 3.059e-07
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
1074.072 1566.838
sample estimates:
mean difference
1320.455
> t.test(pre, post) #WRONG!
Welch Two Sample t-test
data: pre and post
t = 2.6242, df = 19.92, p-value = 0.01629
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
270.5633 2370.3458
sample estimates:
mean of x mean of y
6753.636 5433.182
> wilcox.test(pre, post, paired=T, exact=FALSE)
Wilcoxon signed rank test with continuity correction
data: pre and post
V = 66, p-value = 0.00384
alternative hypothesis: true location shift is not equal to 0
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> attach(thuesen)
> lm(short.velocity~blood.glucose)
Call:
lm(formula = short.velocity ~ blood.glucose)
Coefficients:
(Intercept) blood.glucose
1.09781 0.02196
> summary(lm(short.velocity~blood.glucose))
Call:
lm(formula = short.velocity ~ blood.glucose)
Residuals:
Min 1Q Median 3Q Max
-0.40141 -0.14760 -0.02202 0.03001 0.43490
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.09781 0.11748 9.345 6.26e-09 ***
blood.glucose 0.02196 0.01045 2.101 0.0479 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.2167 on 21 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1737, Adjusted R-squared: 0.1343
F-statistic: 4.414 on 1 and 21 DF, p-value: 0.0479
> summary(lm(short.velocity~blood.glucose))
Call:
lm(formula = short.velocity ~ blood.glucose)
Residuals:
Min 1Q Median 3Q Max
-0.40141 -0.14760 -0.02202 0.03001 0.43490
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.09781 0.11748 9.345 6.26e-09 ***
blood.glucose 0.02196 0.01045 2.101 0.0479 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.2167 on 21 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1737, Adjusted R-squared: 0.1343
F-statistic: 4.414 on 1 and 21 DF, p-value: 0.0479
> plot(blood.glucose,short.velocity)
> abline(lm(short.velocity~blood.glucose))
> if (.make.epsf) dev.copy2eps(file="velo-gluc-line.ps")
> lm.velo <- lm(short.velocity~blood.glucose)
> fitted(lm.velo)
1 2 3 4 5 6 7
1.433841 1.335010 1.275711 1.526084 1.255945 1.214216 1.302066
8 9 10 11 12 13 14
1.341599 1.262534 1.365758 1.244964 1.212020 1.515103 1.429449
15 17 18 19 20 21 22
1.244964 1.190057 1.324029 1.372346 1.451411 1.389916 1.205431
23 24
1.291085 1.306459
> resid(lm.velo)
1 2 3 4 5
0.326158532 0.004989882 -0.005711308 -0.056084062 0.014054962
6 7 8 9 10
0.275783754 0.007933665 -0.251598875 -0.082533795 -0.145757649
11 12 13 14 15
0.005036223 -0.022019994 0.434897199 -0.149448964 0.275036223
17 18 19 20 21
-0.070057471 0.045971143 -0.182346406 -0.401411486 -0.069916424
22 23 24
-0.175431237 -0.171085074 0.393541161
> options(error=expression(NULL))
> plot(blood.glucose,short.velocity)
> lines(blood.glucose,fitted(lm.velo))
Error in xy.coords(x, y) : 'x' and 'y' lengths differ
Calls: lines -> lines.default -> plot.xy -> xy.coords
> options(error=NULL)
> lines(blood.glucose[!is.na(short.velocity)],fitted(lm.velo))
> cc <- complete.cases(thuesen)
> options(na.action=na.exclude)
> lm.velo <- lm(short.velocity~blood.glucose)
> fitted(lm.velo)
1 2 3 4 5 6 7
1.433841 1.335010 1.275711 1.526084 1.255945 1.214216 1.302066
8 9 10 11 12 13 14
1.341599 1.262534 1.365758 1.244964 1.212020 1.515103 1.429449
15 16 17 18 19 20 21
1.244964 NA 1.190057 1.324029 1.372346 1.451411 1.389916
22 23 24
1.205431 1.291085 1.306459
> segments(blood.glucose,fitted(lm.velo),
+ blood.glucose,short.velocity)
> if (.make.epsf) dev.copy2eps(file="velo-gluc-seg.ps")
> plot(fitted(lm.velo),resid(lm.velo))
> if (.make.epsf) dev.copy2eps(file="velo-gluc-resid.ps")
> qqnorm(resid(lm.velo))
> if (.make.epsf) dev.copy2eps(file="velo-gluc-qqnorm.ps")
> predict(lm.velo)
1 2 3 4 5 6 7
1.433841 1.335010 1.275711 1.526084 1.255945 1.214216 1.302066
8 9 10 11 12 13 14
1.341599 1.262534 1.365758 1.244964 1.212020 1.515103 1.429449
15 16 17 18 19 20 21
1.244964 NA 1.190057 1.324029 1.372346 1.451411 1.389916
22 23 24
1.205431 1.291085 1.306459
> predict(lm.velo,int="c")
fit lwr upr
1 1.433841 1.291371 1.576312
2 1.335010 1.240589 1.429431
3 1.275711 1.169536 1.381887
4 1.526084 1.306561 1.745607
5 1.255945 1.139367 1.372523
6 1.214216 1.069315 1.359118
7 1.302066 1.205244 1.398889
8 1.341599 1.246317 1.436881
9 1.262534 1.149694 1.375374
10 1.365758 1.263750 1.467765
11 1.244964 1.121641 1.368287
12 1.212020 1.065457 1.358583
13 1.515103 1.305352 1.724854
14 1.429449 1.290217 1.568681
15 1.244964 1.121641 1.368287
16 NA NA NA
17 1.190057 1.026217 1.353898
18 1.324029 1.230050 1.418008
19 1.372346 1.267629 1.477064
20 1.451411 1.295446 1.607377
21 1.389916 1.276444 1.503389
22 1.205431 1.053805 1.357057
23 1.291085 1.191084 1.391086
24 1.306459 1.210592 1.402326
> predict(lm.velo,int="p")
fit lwr upr
1 1.433841 0.9612137 1.906469
2 1.335010 0.8745815 1.795439
3 1.275711 0.8127292 1.738693
4 1.526084 1.0248161 2.027352
5 1.255945 0.7904672 1.721423
6 1.214216 0.7408499 1.687583
7 1.302066 0.8411393 1.762993
8 1.341599 0.8809929 1.802205
9 1.262534 0.7979780 1.727090
10 1.365758 0.9037136 1.827802
11 1.244964 0.7777510 1.712177
12 1.212020 0.7381424 1.685898
13 1.515103 1.0180367 2.012169
14 1.429449 0.9577873 1.901111
15 1.244964 0.7777510 1.712177
16 NA NA NA
17 1.190057 0.7105546 1.669560
18 1.324029 0.8636906 1.784367
19 1.372346 0.9096964 1.834996
20 1.451411 0.9745421 1.928281
21 1.389916 0.9252067 1.854626
22 1.205431 0.7299634 1.680899
23 1.291085 0.8294798 1.752690
24 1.306459 0.8457315 1.767186
Warning message:
In predict.lm(lm.velo, int = "p") :
predictions on current data refer to _future_ responses
> pred.frame <- data.frame(blood.glucose=4:20)
> pp <- predict(lm.velo, int="p", newdata=pred.frame)
> pc <- predict(lm.velo, int="c", newdata=pred.frame)
> plot(blood.glucose,short.velocity,
+ ylim=range(short.velocity, pp, na.rm=T))
> pred.gluc <- pred.frame$blood.glucose
> matlines(pred.gluc, pc, lty=c(1,2,2), col="black")
> matlines(pred.gluc, pp, lty=c(1,3,3), col="black")
> if (.make.epsf) dev.copy2eps(file="velo-gluc-final.ps")
> options(error=expression(NULL))
> cor(blood.glucose,short.velocity)
[1] NA
> options(error=NULL)
> cor(blood.glucose,short.velocity,use="complete.obs")
[1] 0.4167546
> cor(thuesen,use="complete.obs")
blood.glucose short.velocity
blood.glucose 1.0000000 0.4167546
short.velocity 0.4167546 1.0000000
> cor.test(blood.glucose,short.velocity)
Pearson's product-moment correlation
data: blood.glucose and short.velocity
t = 2.101, df = 21, p-value = 0.0479
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.005496682 0.707429479
sample estimates:
cor
0.4167546
> cor.test(blood.glucose,short.velocity,method="spearman")
Spearman's rank correlation rho
data: blood.glucose and short.velocity
S = 1380.4, p-value = 0.1392
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.318002
Warning message:
In cor.test.default(blood.glucose, short.velocity, method = "spearman") :
cannot compute exact p-value with ties
> cor.test(blood.glucose,short.velocity,method="kendall")
Kendall's rank correlation tau
data: blood.glucose and short.velocity
z = 1.5604, p-value = 0.1187
alternative hypothesis: true tau is not equal to 0
sample estimates:
tau
0.2350616
Warning message:
In cor.test.default(blood.glucose, short.velocity, method = "kendall") :
cannot compute exact p-value with ties
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> attach(red.cell.folate)
> summary(red.cell.folate)
folate ventilation
Min. :206.0 N2O+O2,24h:8
1st Qu.:249.5 N2O+O2,op :9
Median :274.0 O2,24h :5
Mean :283.2
3rd Qu.:305.5
Max. :392.0
> anova(lm(folate~ventilation))
Analysis of Variance Table
Response: folate
Df Sum Sq Mean Sq F value Pr(>F)
ventilation 2 15516 7757.9 3.7113 0.04359 *
Residuals 19 39716 2090.3
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> attach(juul)
> anova(lm(igf1~tanner)) ## WRONG!
Analysis of Variance Table
Response: igf1
Df Sum Sq Mean Sq F value Pr(>F)
tanner 1 10985605 10985605 686.07 < 2.2e-16 ***
Residuals 790 12649728 16012
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> juul$tanner <- factor(juul$tanner,
+ labels=c("I","II","III","IV","V"))
> detach(juul)
> attach(juul)
> summary(tanner)
I II III IV V NAs
515 103 72 81 328 240
> anova(lm(igf1~tanner))
Analysis of Variance Table
Response: igf1
Df Sum Sq Mean Sq F value Pr(>F)
tanner 4 12696217 3174054 228.35 < 2.2e-16 ***
Residuals 787 10939116 13900
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> summary(lm(folate~ventilation))
Call:
lm(formula = folate ~ ventilation)
Residuals:
Min 1Q Median 3Q Max
-73.625 -35.361 -4.444 35.625 75.375
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 316.62 16.16 19.588 4.65e-14 ***
ventilationN2O+O2,op -60.18 22.22 -2.709 0.0139 *
ventilationO2,24h -38.62 26.06 -1.482 0.1548
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 45.72 on 19 degrees of freedom
Multiple R-squared: 0.2809, Adjusted R-squared: 0.2052
F-statistic: 3.711 on 2 and 19 DF, p-value: 0.04359
> pairwise.t.test(folate, ventilation, p.adj="bonferroni")
Pairwise comparisons using t tests with pooled SD
data: folate and ventilation
N2O+O2,24h N2O+O2,op
N2O+O2,op 0.042 -
O2,24h 0.464 1.000
P value adjustment method: bonferroni
> pairwise.t.test(folate,ventilation)
Pairwise comparisons using t tests with pooled SD
data: folate and ventilation
N2O+O2,24h N2O+O2,op
N2O+O2,op 0.042 -
O2,24h 0.310 0.408
P value adjustment method: holm
> oneway.test(folate~ventilation)
One-way analysis of means (not assuming equal variances)
data: folate and ventilation
F = 2.9704, num df = 2.000, denom df = 11.065, p-value =
0.09277
> pairwise.t.test(folate,ventilation,pool.sd=F)
Pairwise comparisons using t tests with non-pooled SD
data: folate and ventilation
N2O+O2,24h N2O+O2,op
N2O+O2,op 0.087 -
O2,24h 0.321 0.321
P value adjustment method: holm
> xbar <- tapply(folate, ventilation, mean)
> s <- tapply(folate, ventilation, sd)
> n <- tapply(folate, ventilation, length)
> sem <- s/sqrt(n)
> stripchart(folate~ventilation, method="jitter",
+ jitter=0.05, pch=16, vert=T)
> arrows(1:3,xbar+sem,1:3,xbar-sem,angle=90,code=3,length=.1)
> lines(1:3,xbar,pch=4,type="b",cex=2)
> if (.make.epsf) dev.copy2eps(file="oneway.ps")
> bartlett.test(folate~ventilation)
Bartlett test of homogeneity of variances
data: folate by ventilation
Bartlett's K-squared = 2.0951, df = 2, p-value = 0.3508
> kruskal.test(folate~ventilation)
Kruskal-Wallis rank sum test
data: folate by ventilation
Kruskal-Wallis chi-squared = 4.1852, df = 2, p-value =
0.1234
> attach(heart.rate)
> heart.rate
hr subj time
1 96 1 0
2 110 2 0
3 89 3 0
4 95 4 0
5 128 5 0
6 100 6 0
7 72 7 0
8 79 8 0
9 100 9 0
10 92 1 30
11 106 2 30
12 86 3 30
13 78 4 30
14 124 5 30
15 98 6 30
16 68 7 30
17 75 8 30
18 106 9 30
19 86 1 60
20 108 2 60
21 85 3 60
22 78 4 60
23 118 5 60
24 100 6 60
25 67 7 60
26 74 8 60
27 104 9 60
28 92 1 120
29 114 2 120
30 83 3 120
31 83 4 120
32 118 5 120
33 94 6 120
34 71 7 120
35 74 8 120
36 102 9 120
> gl(9,1,36)
[1] 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4
[32] 5 6 7 8 9
Levels: 1 2 3 4 5 6 7 8 9
> gl(4,9,36,labels=c(0,30,60,120))
[1] 0 0 0 0 0 0 0 0 0 30 30 30 30 30 30
[16] 30 30 30 60 60 60 60 60 60 60 60 60 120 120 120
[31] 120 120 120 120 120 120
Levels: 0 30 60 120
> anova(lm(hr~subj+time))
Analysis of Variance Table
Response: hr
Df Sum Sq Mean Sq F value Pr(>F)
subj 8 8966.6 1120.82 90.6391 4.863e-16 ***
time 3 151.0 50.32 4.0696 0.01802 *
Residuals 24 296.8 12.37
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> interaction.plot(time, subj, hr)
> if (.make.epsf) dev.copy2eps(file="interaction-plot.ps")
> interaction.plot(ordered(time),subj,hr)
> friedman.test(hr~time|subj,data=heart.rate)
Friedman rank sum test
data: hr and time and subj
Friedman chi-squared = 8.5059, df = 3, p-value = 0.03664
> attach(thuesen)
> lm.velo <- lm(short.velocity~blood.glucose)
> anova(lm.velo)
Analysis of Variance Table
Response: short.velocity
Df Sum Sq Mean Sq F value Pr(>F)
blood.glucose 1 0.20727 0.207269 4.414 0.0479 *
Residuals 21 0.98610 0.046957
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> prop.test(39,215,.15)
1-sample proportions test with continuity correction
data: 39 out of 215, null probability 0.15
X-squared = 1.425, df = 1, p-value = 0.2326
alternative hypothesis: true p is not equal to 0.15
95 percent confidence interval:
0.1335937 0.2408799
sample estimates:
p
0.1813953
> binom.test(39,215,.15)
Exact binomial test
data: 39 and 215
number of successes = 39, number of trials = 215, p-value
= 0.2135
alternative hypothesis: true probability of success is not equal to 0.15
95 percent confidence interval:
0.1322842 0.2395223
sample estimates:
probability of success
0.1813953
> lewitt.machin.success <- c(9,4)
> lewitt.machin.total <- c(12,13)
> prop.test(lewitt.machin.success,lewitt.machin.total)
2-sample test for equality of proportions with continuity
correction
data: lewitt.machin.success out of lewitt.machin.total
X-squared = 3.2793, df = 1, p-value = 0.07016
alternative hypothesis: two.sided
95 percent confidence interval:
0.01151032 0.87310506
sample estimates:
prop 1 prop 2
0.7500000 0.3076923
> matrix(c(9,4,3,9),2)
[,1] [,2]
[1,] 9 3
[2,] 4 9
> lewitt.machin <- matrix(c(9,4,3,9),2)
> fisher.test(lewitt.machin)
Fisher's Exact Test for Count Data
data: lewitt.machin
p-value = 0.04718
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.9006803 57.2549701
sample estimates:
odds ratio
6.180528
> chisq.test(lewitt.machin)
Pearson's Chi-squared test with Yates' continuity
correction
data: lewitt.machin
X-squared = 3.2793, df = 1, p-value = 0.07016
> caesar.shoe
<4 4 4.5 5 5.5 6+
Yes 5 7 6 7 8 10
No 17 28 36 41 46 140
> caesar.shoe.yes <- caesar.shoe["Yes",]
> caesar.shoe.total <- margin.table(caesar.shoe,2)
> caesar.shoe.yes
<4 4 4.5 5 5.5 6+
5 7 6 7 8 10
> caesar.shoe.total
<4 4 4.5 5 5.5 6+
22 35 42 48 54 150
> prop.test(caesar.shoe.yes,caesar.shoe.total)
6-sample test for equality of proportions without
continuity correction
data: caesar.shoe.yes out of caesar.shoe.total
X-squared = 9.2874, df = 5, p-value = 0.09814
alternative hypothesis: two.sided
sample estimates:
prop 1 prop 2 prop 3 prop 4 prop 5 prop 6
0.22727273 0.20000000 0.14285714 0.14583333 0.14814815 0.06666667
Warning message:
In prop.test(caesar.shoe.yes, caesar.shoe.total) :
Chi-squared approximation may be incorrect
> prop.trend.test(caesar.shoe.yes,caesar.shoe.total)
Chi-squared Test for Trend in Proportions
data: caesar.shoe.yes out of caesar.shoe.total ,
using scores: 1 2 3 4 5 6
X-squared = 8.0237, df = 1, p-value = 0.004617
> caff.marital <- matrix(c(652,1537,598,242,36,46,38,21,218
+ ,327,106,67),
+ nrow=3,byrow=T)
> colnames(caff.marital) <- c("0","1-150","151-300",">300")
> rownames(caff.marital) <- c("Married","Prev.married","Single")
> caff.marital
0 1-150 151-300 >300
Married 652 1537 598 242
Prev.married 36 46 38 21
Single 218 327 106 67
> chisq.test(caff.marital)
Pearson's Chi-squared test
data: caff.marital
X-squared = 51.656, df = 6, p-value = 2.187e-09
> chisq.test(caff.marital)$expected
0 1-150 151-300 >300
Married 705.83179 1488.01183 578.06533 257.09105
Prev.married 32.85648 69.26698 26.90895 11.96759
Single 167.31173 352.72119 137.02572 60.94136
> chisq.test(caff.marital)$observed
0 1-150 151-300 >300
Married 652 1537 598 242
Prev.married 36 46 38 21
Single 218 327 106 67
> E <- chisq.test(caff.marital)$expected
> O <- chisq.test(caff.marital)$observed
> (O-E)^2/E
0 1-150 151-300 >300
Married 4.1055981 1.612783 0.6874502 0.8858331
Prev.married 0.3007537 7.815444 4.5713926 6.8171090
Single 15.3563704 1.875645 7.0249243 0.6023355
> attach(juul)
> chisq.test(tanner,sex)
Pearson's Chi-squared test
data: tanner and sex
X-squared = 28.867, df = 4, p-value = 8.318e-06
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> curve(pt(x,25,ncp=3), from=0, to=6)
> abline(v=qt(.975,25))
> if (.make.epsf) dev.copy2eps(file="noncentral-t.ps")
> pt(qt(.975,25),25,ncp=3)
[1] 0.1779891
> power.t.test(delta=0.5, sd=2, sig.level = 0.01, power=0.9)
Two-sample t test power calculation
n = 477.8021
delta = 0.5
sd = 2
sig.level = 0.01
power = 0.9
alternative = two.sided
NOTE: n is number in *each* group
> power.t.test(n=450, delta=0.5, sd=2, sig.level = 0.01)
Two-sample t test power calculation
n = 450
delta = 0.5
sd = 2
sig.level = 0.01
power = 0.8784433
alternative = two.sided
NOTE: n is number in *each* group
> power.t.test(delta=0.5, sd=2, sig.level = 0.01, power=0.9,
+ alt="one.sided")
Two-sample t test power calculation
n = 417.898
delta = 0.5
sd = 2
sig.level = 0.01
power = 0.9
alternative = one.sided
NOTE: n is number in *each* group
> power.t.test(delta=10, sd=10*sqrt(2), power=0.85, type="paired")
Paired t test power calculation
n = 19.96892
delta = 10
sd = 14.14214
sig.level = 0.05
power = 0.85
alternative = two.sided
NOTE: n is number of *pairs*, sd is std.dev. of *differences* within pairs
> power.prop.test(power=.85,p1=.15,p2=.30)
Two-sample comparison of proportions power calculation
n = 137.604
p1 = 0.15
p2 = 0.3
sig.level = 0.05
power = 0.85
alternative = two.sided
NOTE: n is number in *each* group
> ### no data sets in exercises
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> age <- subset(juul, age >= 10 & age <= 16)$age
> range(age)
[1] 10.01 16.00
> agegr <- cut(age, seq(10,16,2), right=F, include.lowest=T)
> length(age)
[1] 502
> table(agegr)
agegr
[10,12) [12,14) [14,16]
190 168 144
> agegr2 <- cut(age, seq(10,16,2), right=F)
> table(agegr2)
agegr2
[10,12) [12,14) [14,16)
190 168 142
> q <- quantile(age, c(0, .25, .50, .75, 1))
> q
0% 25% 50% 75% 100%
10.0100 11.3825 12.6400 14.2275 16.0000
> ageQ <- cut(age, q, include.lowest=T)
> table(ageQ)
ageQ
[10,11.4] (11.4,12.6] (12.6,14.2] (14.2,16]
126 125 125 126
> levels(ageQ) <- c("1st", "2nd", "3rd", "4th")
> levels(agegr) <- c("10-11", "12-13", "14-15")
> pain <- c(0,3,2,2,1)
> fpain <- factor(pain,levels=0:3,
+ labels=c("none","mild","medium","severe"))
> text.pain <- c("none","severe", "medium", "medium", "mild")
> factor(text.pain)
[1] none severe medium medium mild
Levels: medium mild none severe
> ftpain <- factor(text.pain)
> ftpain2 <- factor(ftpain,
+ levels=c("none", "mild", "medium", "severe"))
> ftpain3 <- ftpain2
> levels(ftpain3) <- list(
+ none="none",
+ intermediate=c("mild","medium"),
+ severe="severe")
> ftpain3
[1] none severe intermediate intermediate
[5] intermediate
Levels: none intermediate severe
> ftpain4 <- ftpain2
> levels(ftpain4) <- c("none","intermediate","intermediate","severe")
> ftpain4
[1] none severe intermediate intermediate
[5] intermediate
Levels: none intermediate severe
> stroke <- read.csv2(
+ system.file("rawdata","stroke.csv", package="ISwR"),
+ na.strings=".")
> names(stroke) <- tolower(names(stroke))
> head(stroke)
sex died dstr age dgn coma diab minf han
1 1 7.01.1991 2.01.1991 76 INF 0 0 1 0
2 1 <NA> 3.01.1991 58 INF 0 0 0 0
3 1 2.06.1991 8.01.1991 74 INF 0 0 1 1
4 0 13.01.1991 11.01.1991 77 ICH 0 1 0 1
5 0 23.01.1996 13.01.1991 76 INF 0 1 0 1
6 1 13.01.1991 13.01.1991 48 ICH 1 0 0 1
> stroke <- transform(stroke,
+ died = as.Date(died, format="%d.%m.%Y"),
+ dstr = as.Date(dstr, format="%d.%m.%Y"))
> summary(stroke$died)
Min. 1st Qu. Median Mean 3rd Qu.
"1991-01-07" "1992-03-14" "1993-01-23" "1993-02-15" "1993-11-04"
Max. NAs
"1996-02-22" "338"
> summary(stroke$dstr)
Min. 1st Qu. Median Mean 3rd Qu.
"1991-01-02" "1991-11-08" "1992-08-12" "1992-07-27" "1993-04-30"
Max.
"1993-12-31"
> summary(stroke$died - stroke$dstr)
Time differences in days
Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
0.0 8.0 28.0 225.7 268.5 1836.0 338
> head(stroke$died - stroke$dstr)
Time differences in days
[1] 5 NA 145 2 1836 0
> o <- options(width=60) # minor cheat for visual purposes
> stroke <- transform(stroke,
+ end = pmin(died, as.Date("1996-1-1"), na.rm = T),
+ dead = !is.na(died) & died < as.Date("1996-1-1"))
> head(stroke)
sex died dstr age dgn coma diab minf han
1 1 1991-01-07 1991-01-02 76 INF 0 0 1 0
2 1 <NA> 1991-01-03 58 INF 0 0 0 0
3 1 1991-06-02 1991-01-08 74 INF 0 0 1 1
4 0 1991-01-13 1991-01-11 77 ICH 0 1 0 1
5 0 1996-01-23 1991-01-13 76 INF 0 1 0 1
6 1 1991-01-13 1991-01-13 48 ICH 1 0 0 1
end dead
1 1991-01-07 TRUE
2 1996-01-01 FALSE
3 1991-06-02 TRUE
4 1991-01-13 TRUE
5 1996-01-01 FALSE
6 1991-01-13 TRUE
> options(o); rm(o)
> stroke <- transform(stroke,
+ obstime = as.numeric(end - dstr, units="days")/365.25)
> rawstroke <- read.csv2(
+ system.file("rawdata","stroke.csv", package="ISwR"),
+ na.strings=".")
> ix <- c("DSTR", "DIED")
> rawstroke[ix] <- lapply(rawstroke[ix],
+ as.Date, format="%d.%m.%Y")
> head(rawstroke)
SEX DIED DSTR AGE DGN COMA DIAB MINF HAN
1 1 1991-01-07 1991-01-02 76 INF 0 0 1 0
2 1 <NA> 1991-01-03 58 INF 0 0 0 0
3 1 1991-06-02 1991-01-08 74 INF 0 0 1 1
4 0 1991-01-13 1991-01-11 77 ICH 0 1 0 1
5 0 1996-01-23 1991-01-13 76 INF 0 1 0 1
6 1 1991-01-13 1991-01-13 48 ICH 1 0 0 1
> ix <- 6:9
> rawstroke[ix] <- lapply(rawstroke[ix],
+ factor, levels=0:1, labels=c("No","Yes"))
> strokesub <- ISwR::stroke[1:10,2:3]
> strokesub
died dstr
1 1991-01-07 1991-01-02
2 <NA> 1991-01-03
3 1991-06-02 1991-01-08
4 1991-01-13 1991-01-11
5 <NA> 1991-01-13
6 1991-01-13 1991-01-13
7 1993-12-01 1991-01-14
8 1991-12-12 1991-01-14
9 <NA> 1991-01-15
10 1993-11-10 1991-01-15
> strokesub <- transform(strokesub,
+ event = !is.na(died))
> strokesub <- transform(strokesub,
+ obstime = ifelse(event, died-dstr, as.Date("1996-1-1") - dstr))
> strokesub
died dstr event obstime
1 1991-01-07 1991-01-02 TRUE 5
2 <NA> 1991-01-03 FALSE 1824
3 1991-06-02 1991-01-08 TRUE 145
4 1991-01-13 1991-01-11 TRUE 2
5 <NA> 1991-01-13 FALSE 1814
6 1991-01-13 1991-01-13 TRUE 0
7 1993-12-01 1991-01-14 TRUE 1052
8 1991-12-12 1991-01-14 TRUE 332
9 <NA> 1991-01-15 FALSE 1812
10 1993-11-10 1991-01-15 TRUE 1030
> juulgrl <- subset(juul, sex==2, select=-c(testvol,sex))
> juulboy <- subset(juul, sex==1, select=-c(menarche,sex))
> juulgrl$sex <- factor("F")
> juulgrl$testvol <- NA
> juulboy$sex <- factor("M")
> juulboy$menarche <- NA
> juulall <- rbind(juulboy, juulgrl)
> names(juulall)
[1] "age" "igf1" "tanner" "testvol" "sex"
[6] "menarche"
> levels(juulall$sex)
[1] "M" "F"
> head(nickel)
id icd exposure dob age1st agein ageout
1 3 0 5 1889.019 17.4808 45.2273 92.9808
2 4 162 5 1885.978 23.1864 48.2684 63.2712
3 6 163 10 1881.255 25.2452 52.9917 54.1644
4 8 527 9 1886.340 24.7206 47.9067 69.6794
5 9 150 0 1879.500 29.9575 54.7465 76.8442
6 10 163 2 1889.915 21.2877 44.3314 62.5413
> head(ewrates)
year age lung nasal other
1 1931 10 1 0 1269
2 1931 15 2 0 2201
3 1931 20 6 0 3116
4 1931 25 14 0 3024
5 1931 30 30 1 3188
6 1931 35 68 1 4165
> nickel <- transform(nickel,
+ agr = trunc(agein/5)*5,
+ ygr = trunc((dob+agein-1)/5)*5+1)
> mrg <- merge(nickel, ewrates,
+ by.x=c("agr","ygr"), by.y=c("age","year"))
> head(mrg,10)
agr ygr id icd exposure dob age1st agein ageout
1 20 1931 273 154 0 1909.500 14.6913 24.7465 55.9302
2 20 1931 213 162 0 1910.129 14.2018 24.1177 63.0493
3 20 1931 546 0 0 1909.500 14.4945 24.7465 72.5000
4 20 1931 574 491 0 1909.729 14.0356 24.5177 70.6592
5 20 1931 110 0 0 1909.247 14.0302 24.9999 72.7534
6 20 1931 325 434 0 1910.500 14.0737 23.7465 43.0343
7 25 1931 56 502 2 1904.500 18.2917 29.7465 51.5847
8 25 1931 690 420 0 1906.500 17.2206 27.7465 55.1219
9 25 1931 443 420 0 1905.326 14.5562 28.9204 65.7616
10 25 1931 137 465 0 1905.386 19.0808 28.8601 74.2794
lung nasal other
1 6 0 3116
2 6 0 3116
3 6 0 3116
4 6 0 3116
5 6 0 3116
6 6 0 3116
7 14 0 3024
8 14 0 3024
9 14 0 3024
10 14 0 3024
> head(alkfos)
grp c0 c3 c6 c9 c12 c18 c24
1 1 142 140 159 162 152 175 148
2 1 120 126 120 146 134 119 116
3 1 175 161 168 164 213 194 221
4 1 234 203 174 197 289 174 189
5 1 94 107 146 124 128 98 114
6 1 128 97 113 203 NA NA NA
> a2 <- alkfos
> names(a2) <- sub("c", "c.", names(a2))
> names(a2)
[1] "grp" "c.0" "c.3" "c.6" "c.9" "c.12" "c.18" "c.24"
> a.long <- reshape(a2, varying=2:8, direction="long")
> head(a.long)
grp time c id
1.0 1 0 142 1
2.0 1 0 120 2
3.0 1 0 175 3
4.0 1 0 234 4
5.0 1 0 94 5
6.0 1 0 128 6
> tail(a.long)
grp time c id
38.24 2 24 95 38
39.24 2 24 NA 39
40.24 2 24 192 40
41.24 2 24 94 41
42.24 2 24 194 42
43.24 2 24 129 43
> o <- with(a.long, order(id, time))
> head(a.long[o,], 10)
grp time c id
1.0 1 0 142 1
1.3 1 3 140 1
1.6 1 6 159 1
1.9 1 9 162 1
1.12 1 12 152 1
1.18 1 18 175 1
1.24 1 24 148 1
2.0 1 0 120 2
2.3 1 3 126 2
2.6 1 6 120 2
> a.long2 <- na.omit(a.long)
> attr(a.long2, "reshapeLong") <- NULL
> a.wide2 <- reshape(a.long2, direction="wide", v.names="c",
+ idvar="id", timevar="time")
> head(a.wide2)
grp id c.0 c.3 c.6 c.9 c.12 c.18 c.24
1.0 1 1 142 140 159 162 152 175 148
2.0 1 2 120 126 120 146 134 119 116
3.0 1 3 175 161 168 164 213 194 221
4.0 1 4 234 203 174 197 289 174 189
5.0 1 5 94 107 146 124 128 98 114
6.0 1 6 128 97 113 203 NA NA NA
> l <- split(a.long$c, a.long$id)
> l[1:3]
$`1`
[1] 142 140 159 162 152 175 148
$`2`
[1] 120 126 120 146 134 119 116
$`3`
[1] 175 161 168 164 213 194 221
> l2 <- lapply(l, function(x) x / x[1])
> a.long$c.adj <- unsplit(l2, a.long$id)
> subset(a.long, id==1)
grp time c id c.adj
1.0 1 0 142 1 1.0000000
1.3 1 3 140 1 0.9859155
1.6 1 6 159 1 1.1197183
1.9 1 9 162 1 1.1408451
1.12 1 12 152 1 1.0704225
1.18 1 18 175 1 1.2323944
1.24 1 24 148 1 1.0422535
> a.long$c.adj <- ave(a.long$c, a.long$id,
+ FUN = function(x) x / x[1])
> all.equal(unsplit(l2, a.long$id), a.long$c.adj)
[1] TRUE
> l <- split(a.long, a.long$id)
> l2 <- lapply(l, transform, c.adj = c / c[1])
> a.long2 <- unsplit(l2, a.long$id)
> all.equal(a.long2$c.adj, a.long$c.adj)
[1] TRUE
> head(nickel)
id icd exposure dob age1st agein ageout agr ygr
1 3 0 5 1889.019 17.4808 45.2273 92.9808 45 1931
2 4 162 5 1885.978 23.1864 48.2684 63.2712 45 1931
3 6 163 10 1881.255 25.2452 52.9917 54.1644 50 1931
4 8 527 9 1886.340 24.7206 47.9067 69.6794 45 1931
5 9 150 0 1879.500 29.9575 54.7465 76.8442 50 1931
6 10 163 2 1889.915 21.2877 44.3314 62.5413 40 1931
> entry <- pmax(nickel$agein, 60)
> exit <- pmin(nickel$ageout, 65)
> valid <- (entry < exit)
> entry <- entry[valid]
> exit <- exit[valid]
> cens <- (nickel$ageout[valid] > 65)
> nickel60 <- nickel[valid,]
> nickel60$icd[cens] <- 0
> nickel60$agein <- entry
> nickel60$ageout <- exit
> nickel60$agr <- 60
> nickel60$ygr <- with(nickel60, trunc((dob+agein-1)/5)*5+1)
> head(nickel60)
id icd exposure dob age1st agein ageout agr ygr
1 3 0 5 1889.019 17.4808 60 65.0000 60 1946
2 4 162 5 1885.978 23.1864 60 63.2712 60 1941
4 8 0 9 1886.340 24.7206 60 65.0000 60 1946
5 9 0 0 1879.500 29.9575 60 65.0000 60 1936
6 10 163 2 1889.915 21.2877 60 62.5413 60 1946
7 15 334 0 1890.500 23.2836 60 62.0000 60 1946
> trim <- function(start)
+ {
+ end <- start + 5
+ entry <- pmax(nickel$agein, start)
+ exit <- pmin(nickel$ageout, end)
+ valid <- (entry < exit)
+ cens <- (nickel$ageout[valid] > end)
+ result <- nickel[valid,]
+ result$icd[cens] <- 0
+ result$agein <- entry[valid]
+ result$ageout <- exit[valid]
+ result$agr <- start
+ result$ygr <- with(result, trunc((dob+agein-1)/5)*5+1)
+ result
+ }
> head(trim(60))
id icd exposure dob age1st agein ageout agr ygr
1 3 0 5 1889.019 17.4808 60 65.0000 60 1946
2 4 162 5 1885.978 23.1864 60 63.2712 60 1941
4 8 0 9 1886.340 24.7206 60 65.0000 60 1946
5 9 0 0 1879.500 29.9575 60 65.0000 60 1936
6 10 163 2 1889.915 21.2877 60 62.5413 60 1946
7 15 334 0 1890.500 23.2836 60 62.0000 60 1946
> nickel.expand <- do.call("rbind", lapply(seq(20,95,5), trim))
> head(nickel.expand)
id icd exposure dob age1st agein ageout agr ygr
84 110 0 0 1909.247 14.0302 24.9999 25 20 1931
156 213 0 0 1910.129 14.2018 24.1177 25 20 1931
197 273 0 0 1909.500 14.6913 24.7465 25 20 1931
236 325 0 0 1910.500 14.0737 23.7465 25 20 1931
384 546 0 0 1909.500 14.4945 24.7465 25 20 1931
400 574 0 0 1909.729 14.0356 24.5177 25 20 1931
> subset(nickel.expand, id==4)
id icd exposure dob age1st agein ageout agr ygr
2 4 0 5 1885.978 23.1864 48.2684 50.0000 45 1931
2100 4 0 5 1885.978 23.1864 50.0000 55.0000 50 1931
2102 4 0 5 1885.978 23.1864 55.0000 60.0000 55 1936
2104 4 162 5 1885.978 23.1864 60.0000 63.2712 60 1941
> nickel.expand <- merge(nickel.expand, ewrates,
+ by.x=c("agr","ygr"), by.y=c("age","year"))
> head(nickel.expand)
agr ygr id icd exposure dob age1st agein ageout lung
1 20 1931 325 0 0 1910.500 14.0737 23.7465 25 6
2 20 1931 273 0 0 1909.500 14.6913 24.7465 25 6
3 20 1931 110 0 0 1909.247 14.0302 24.9999 25 6
4 20 1931 574 0 0 1909.729 14.0356 24.5177 25 6
5 20 1931 213 0 0 1910.129 14.2018 24.1177 25 6
6 20 1931 546 0 0 1909.500 14.4945 24.7465 25 6
nasal other
1 0 3116
2 0 3116
3 0 3116
4 0 3116
5 0 3116
6 0 3116
> all.equal(nickel.expand, ISwR::nickel.expand)
[1] TRUE
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> par(mex=0.5)
> pairs(cystfibr, gap=0, cex.labels=0.9)
> if (.make.epsf) dev.copy2eps(file="cyst-fibr.ps",height=4.5,width=4.49)
> attach(cystfibr)
The following object is masked from package:ISwR:
tlc
> if (exists("age",.GlobalEnv,inh=F)) rm(age)
> if (exists("height",.GlobalEnv,inh=F)) rm(height)
> if (exists("weight",.GlobalEnv,inh=F)) rm(weight)
> summary(lm(pemax~age+sex+height+weight+bmp+fev1+rv+frc+tlc))
Call:
lm(formula = pemax ~ age + sex + height + weight + bmp + fev1 +
rv + frc + tlc)
Residuals:
Min 1Q Median 3Q Max
-37.338 -11.532 1.081 13.386 33.405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 176.0582 225.8912 0.779 0.448
age -2.5420 4.8017 -0.529 0.604
sex -3.7368 15.4598 -0.242 0.812
height -0.4463 0.9034 -0.494 0.628
weight 2.9928 2.0080 1.490 0.157
bmp -1.7449 1.1552 -1.510 0.152
fev1 1.0807 1.0809 1.000 0.333
rv 0.1970 0.1962 1.004 0.331
frc -0.3084 0.4924 -0.626 0.540
tlc 0.1886 0.4997 0.377 0.711
Residual standard error: 25.47 on 15 degrees of freedom
Multiple R-squared: 0.6373, Adjusted R-squared: 0.4197
F-statistic: 2.929 on 9 and 15 DF, p-value: 0.03195
> 1-25.5^2/var(pemax)
[1] 0.4183949
> anova(lm(pemax~age+sex+height+weight+bmp+fev1+rv+frc+tlc))
Analysis of Variance Table
Response: pemax
Df Sum Sq Mean Sq F value Pr(>F)
age 1 10098.5 10098.5 15.5661 0.001296 **
sex 1 955.4 955.4 1.4727 0.243680
height 1 155.0 155.0 0.2389 0.632089
weight 1 632.3 632.3 0.9747 0.339170
bmp 1 2862.2 2862.2 4.4119 0.053010 .
fev1 1 1549.1 1549.1 2.3878 0.143120
rv 1 561.9 561.9 0.8662 0.366757
frc 1 194.6 194.6 0.2999 0.592007
tlc 1 92.4 92.4 0.1424 0.711160
Residuals 15 9731.2 648.7
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> 955.4+155.0+632.3+2862.2+1549.1+561.9+194.6+92.4
[1] 7002.9
> 7002.9/8
[1] 875.3625
> 875.36/648.7
[1] 1.349407
> 1-pf(1.349407,8,15)
[1] 0.2935148
> ## Not command output:
> m1<-lm(pemax~age+sex+height+weight+bmp+fev1+rv+frc+tlc)
> m2<-lm(pemax~age)
> anova(m1,m2)
Analysis of Variance Table
Model 1: pemax ~ age + sex + height + weight + bmp + fev1 + rv + frc +
tlc
Model 2: pemax ~ age
Res.Df RSS Df Sum of Sq F Pr(>F)
1 15 9731.2
2 23 16734.2 -8 -7002.9 1.3493 0.2936
> summary(lm(pemax~age+sex+height+weight+bmp+fev1+rv+frc+tlc))
Call:
lm(formula = pemax ~ age + sex + height + weight + bmp + fev1 +
rv + frc + tlc)
Residuals:
Min 1Q Median 3Q Max
-37.338 -11.532 1.081 13.386 33.405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 176.0582 225.8912 0.779 0.448
age -2.5420 4.8017 -0.529 0.604
sex -3.7368 15.4598 -0.242 0.812
height -0.4463 0.9034 -0.494 0.628
weight 2.9928 2.0080 1.490 0.157
bmp -1.7449 1.1552 -1.510 0.152
fev1 1.0807 1.0809 1.000 0.333
rv 0.1970 0.1962 1.004 0.331
frc -0.3084 0.4924 -0.626 0.540
tlc 0.1886 0.4997 0.377 0.711
Residual standard error: 25.47 on 15 degrees of freedom
Multiple R-squared: 0.6373, Adjusted R-squared: 0.4197
F-statistic: 2.929 on 9 and 15 DF, p-value: 0.03195
> summary(lm(pemax~age+sex+height+weight+bmp+fev1+rv+frc))
Call:
lm(formula = pemax ~ age + sex + height + weight + bmp + fev1 +
rv + frc)
Residuals:
Min 1Q Median 3Q Max
-38.072 -10.067 0.113 13.526 36.990
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 221.8055 185.4350 1.196 0.2491
age -3.1346 4.4144 -0.710 0.4879
sex -4.6933 14.8363 -0.316 0.7558
height -0.5428 0.8428 -0.644 0.5286
weight 3.3157 1.7672 1.876 0.0790 .
bmp -1.9403 1.0047 -1.931 0.0714 .
fev1 1.0183 1.0392 0.980 0.3417
rv 0.1857 0.1887 0.984 0.3396
frc -0.2605 0.4628 -0.563 0.5813
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 24.78 on 16 degrees of freedom
Multiple R-squared: 0.6339, Adjusted R-squared: 0.4508
F-statistic: 3.463 on 8 and 16 DF, p-value: 0.01649
> summary(lm(pemax~age+sex+height+weight+bmp+fev1+rv))
Call:
lm(formula = pemax ~ age + sex + height + weight + bmp + fev1 +
rv)
Residuals:
Min 1Q Median 3Q Max
-39.425 -12.391 3.834 14.797 36.693
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 166.71822 154.31294 1.080 0.2951
age -1.81783 3.66773 -0.496 0.6265
sex 0.10239 11.89990 0.009 0.9932
height -0.40981 0.79257 -0.517 0.6118
weight 2.87386 1.55120 1.853 0.0814 .
bmp -1.94971 0.98415 -1.981 0.0640 .
fev1 1.41526 0.74788 1.892 0.0756 .
rv 0.09567 0.09798 0.976 0.3425
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 24.28 on 17 degrees of freedom
Multiple R-squared: 0.6266, Adjusted R-squared: 0.4729
F-statistic: 4.076 on 7 and 17 DF, p-value: 0.008452
> summary(lm(pemax~age+sex+height+weight+bmp+fev1))
Call:
lm(formula = pemax ~ age + sex + height + weight + bmp + fev1)
Residuals:
Min 1Q Median 3Q Max
-43.238 -7.403 -0.081 15.534 36.028
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 260.6313 120.5215 2.163 0.0443 *
age -2.9062 3.4898 -0.833 0.4159
sex -1.2115 11.8083 -0.103 0.9194
height -0.6067 0.7655 -0.793 0.4384
weight 3.3463 1.4719 2.273 0.0355 *
bmp -2.3042 0.9136 -2.522 0.0213 *
fev1 1.0274 0.6329 1.623 0.1219
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 24.24 on 18 degrees of freedom
Multiple R-squared: 0.6057, Adjusted R-squared: 0.4743
F-statistic: 4.608 on 6 and 18 DF, p-value: 0.00529
> summary(lm(pemax~age+sex+height+weight+bmp))
Call:
lm(formula = pemax ~ age + sex + height + weight + bmp)
Residuals:
Min 1Q Median 3Q Max
-43.194 -9.412 -2.425 9.157 40.112
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 280.4482 124.9556 2.244 0.0369 *
age -3.0750 3.6352 -0.846 0.4081
sex -11.5281 10.3720 -1.111 0.2802
height -0.6853 0.7962 -0.861 0.4001
weight 3.5546 1.5281 2.326 0.0312 *
bmp -1.9613 0.9263 -2.117 0.0476 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 25.27 on 19 degrees of freedom
Multiple R-squared: 0.548, Adjusted R-squared: 0.429
F-statistic: 4.606 on 5 and 19 DF, p-value: 0.006388
> summary(lm(pemax~age+height+weight+bmp))
Call:
lm(formula = pemax ~ age + height + weight + bmp)
Residuals:
Min 1Q Median 3Q Max
-41.501 -15.460 -2.838 11.082 42.991
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 274.5307 125.5745 2.186 0.0409 *
age -3.0832 3.6566 -0.843 0.4091
height -0.6985 0.8008 -0.872 0.3934
weight 3.6338 1.5354 2.367 0.0282 *
bmp -1.9621 0.9317 -2.106 0.0480 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 25.41 on 20 degrees of freedom
Multiple R-squared: 0.5186, Adjusted R-squared: 0.4223
F-statistic: 5.386 on 4 and 20 DF, p-value: 0.004137
> summary(lm(pemax~height+weight+bmp))
Call:
lm(formula = pemax ~ height + weight + bmp)
Residuals:
Min 1Q Median 3Q Max
-41.794 -11.764 -1.218 13.202 43.631
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 245.3936 119.8927 2.047 0.0534 .
height -0.8264 0.7808 -1.058 0.3019
weight 2.7717 1.1377 2.436 0.0238 *
bmp -1.4876 0.7375 -2.017 0.0566 .
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 25.24 on 21 degrees of freedom
Multiple R-squared: 0.5015, Adjusted R-squared: 0.4302
F-statistic: 7.041 on 3 and 21 DF, p-value: 0.00187
> summary(lm(pemax~weight+bmp))
Call:
lm(formula = pemax ~ weight + bmp)
Residuals:
Min 1Q Median 3Q Max
-42.924 -13.399 4.361 16.642 48.404
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 124.8297 37.4786 3.331 0.003033 **
weight 1.6403 0.3900 4.206 0.000365 ***
bmp -1.0054 0.5814 -1.729 0.097797 .
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 25.31 on 22 degrees of freedom
Multiple R-squared: 0.4749, Adjusted R-squared: 0.4271
F-statistic: 9.947 on 2 and 22 DF, p-value: 0.0008374
> summary(lm(pemax~weight))
Call:
lm(formula = pemax ~ weight)
Residuals:
Min 1Q Median 3Q Max
-44.30 -22.69 2.23 15.91 48.41
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 63.5456 12.7016 5.003 4.63e-05 ***
weight 1.1867 0.3009 3.944 0.000646 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 26.38 on 23 degrees of freedom
Multiple R-squared: 0.4035, Adjusted R-squared: 0.3776
F-statistic: 15.56 on 1 and 23 DF, p-value: 0.0006457
> summary(lm(pemax~age+weight+height))
Call:
lm(formula = pemax ~ age + weight + height)
Residuals:
Min 1Q Median 3Q Max
-43.675 -21.566 3.229 16.274 48.068
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 64.65555 82.40935 0.785 0.441
age 1.56755 3.14363 0.499 0.623
weight 0.86949 0.85922 1.012 0.323
height -0.07608 0.80278 -0.095 0.925
Residual standard error: 27.41 on 21 degrees of freedom
Multiple R-squared: 0.4118, Adjusted R-squared: 0.3278
F-statistic: 4.901 on 3 and 21 DF, p-value: 0.009776
> summary(lm(pemax~age+height))
Call:
lm(formula = pemax ~ age + height)
Residuals:
Min 1Q Median 3Q Max
-44.817 -17.883 3.815 18.275 53.824
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.8600 68.2493 0.262 0.796
age 2.7178 2.9325 0.927 0.364
height 0.3397 0.6900 0.492 0.627
Residual standard error: 27.43 on 22 degrees of freedom
Multiple R-squared: 0.3831, Adjusted R-squared: 0.3271
F-statistic: 6.832 on 2 and 22 DF, p-value: 0.00492
> summary(lm(pemax~age))
Call:
lm(formula = pemax ~ age)
Residuals:
Min 1Q Median 3Q Max
-48.666 -17.174 6.209 16.209 51.334
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.408 16.657 3.026 0.00601 **
age 4.055 1.088 3.726 0.00111 **
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 26.97 on 23 degrees of freedom
Multiple R-squared: 0.3764, Adjusted R-squared: 0.3492
F-statistic: 13.88 on 1 and 23 DF, p-value: 0.001109
> summary(lm(pemax~height))
Call:
lm(formula = pemax ~ height)
Residuals:
Min 1Q Median 3Q Max
-43.876 -19.306 1.787 18.170 61.464
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -33.2757 40.0445 -0.831 0.41453
height 0.9319 0.2596 3.590 0.00155 **
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 27.34 on 23 degrees of freedom
Multiple R-squared: 0.3591, Adjusted R-squared: 0.3312
F-statistic: 12.89 on 1 and 23 DF, p-value: 0.001549
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> attach(cystfibr)
The following object is masked from package:ISwR:
tlc
> summary(lm(pemax~height+I(height^2)))
Call:
lm(formula = pemax ~ height + I(height^2))
Residuals:
Min 1Q Median 3Q Max
-51.411 -14.932 -2.288 12.787 44.933
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 615.36248 240.95580 2.554 0.0181 *
height -8.08324 3.32052 -2.434 0.0235 *
I(height^2) 0.03064 0.01126 2.721 0.0125 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 24.18 on 22 degrees of freedom
Multiple R-squared: 0.5205, Adjusted R-squared: 0.4769
F-statistic: 11.94 on 2 and 22 DF, p-value: 0.0003081
> pred.frame <- data.frame(height=seq(110,180,2))
> lm.pemax.hq <- lm(pemax~height+I(height^2))
> predict(lm.pemax.hq,interval="pred",newdata=pred.frame)
fit lwr upr
1 96.90026 37.94461 155.8559
2 94.33611 36.82985 151.8424
3 92.01705 35.73077 148.3033
4 89.94307 34.66449 145.2217
5 88.11418 33.65007 142.5783
6 86.53038 32.70806 140.3527
7 85.19166 31.85979 138.5235
8 84.09803 31.12689 137.0692
9 83.24949 30.53064 135.9683
10 82.64604 30.09150 135.2006
11 82.28767 29.82873 134.7466
12 82.17439 29.76004 134.5887
13 82.30620 29.90143 134.7110
14 82.68309 30.26696 135.0992
15 83.30507 30.86877 135.7414
16 84.17214 31.71697 136.6273
17 85.28429 32.81967 137.7489
18 86.64153 34.18298 139.1001
19 88.24386 35.81103 140.6767
20 90.09127 37.70599 142.4766
21 92.18378 39.86804 144.4995
22 94.52137 42.29540 146.7473
23 97.10404 44.98431 149.2238
24 99.93180 47.92894 151.9347
25 103.00465 51.12145 154.8879
26 106.32259 54.55189 158.0933
27 109.88562 58.20818 161.5631
28 113.69373 62.07617 165.3113
29 117.74692 66.13965 169.3542
30 122.04521 70.38050 173.7099
31 126.58858 74.77888 178.3983
32 131.37704 79.31354 183.4405
33 136.41059 83.96224 188.8589
34 141.68922 88.70229 194.6761
35 147.21294 93.51117 200.9147
36 152.98174 98.36718 207.5963
> pp <- predict(lm.pemax.hq,newdata=pred.frame,interval="pred")
> pc <- predict(lm.pemax.hq,newdata=pred.frame,interval="conf")
> plot(height,pemax,ylim=c(0,200))
> matlines(pred.frame$height,pp,lty=c(1,2,2),col="black")
> matlines(pred.frame$height,pc,lty=c(1,3,3),col="black")
> if (.make.epsf) dev.copy2eps(file="pemax-height-quad.ps")
> x <- runif(20)
> y <- 2*x+rnorm(20,0,0.3)
> summary(lm(y~x))
Call:
lm(formula = y ~ x)
Residuals:
Min 1Q Median 3Q Max
-0.35286 -0.19547 -0.02266 0.18194 0.56356
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.2224 0.1249 -1.781 0.0918 .
x 2.4369 0.2039 11.953 5.38e-10 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.2502 on 18 degrees of freedom
Multiple R-squared: 0.8881, Adjusted R-squared: 0.8819
F-statistic: 142.9 on 1 and 18 DF, p-value: 5.376e-10
> summary(lm(y~x-1))
Call:
lm(formula = y ~ x - 1)
Residuals:
Min 1Q Median 3Q Max
-0.45178 -0.20224 -0.01095 0.06261 0.47787
Coefficients:
Estimate Std. Error t value Pr(>|t|)
x 2.11227 0.09642 21.91 6.04e-15 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.2641 on 19 degrees of freedom
Multiple R-squared: 0.9619, Adjusted R-squared: 0.9599
F-statistic: 480 on 1 and 19 DF, p-value: 6.039e-15
> anova(lm(y~x))
Analysis of Variance Table
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
x 1 8.945 8.9450 142.87 5.376e-10 ***
Residuals 18 1.127 0.0626
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> anova(lm(y~x-1))
Analysis of Variance Table
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
x 1 33.484 33.484 479.96 6.039e-15 ***
Residuals 19 1.326 0.070
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> model.matrix(pemax~height+weight)
(Intercept) height weight
1 1 109 13.1
2 1 112 12.9
3 1 124 14.1
4 1 125 16.2
5 1 127 21.5
6 1 130 17.5
7 1 139 30.7
8 1 150 28.4
9 1 146 25.1
10 1 155 31.5
11 1 156 39.9
12 1 153 42.1
13 1 160 45.6
14 1 158 51.2
15 1 160 35.9
16 1 153 34.8
17 1 174 44.7
18 1 176 60.1
19 1 171 42.6
20 1 156 37.2
21 1 174 54.6
22 1 178 64.0
23 1 180 73.8
24 1 175 51.1
25 1 179 71.5
attr(,"assign")
[1] 0 1 2
> attach(red.cell.folate)
> model.matrix(folate~ventilation)
(Intercept) ventilationN2O+O2,op ventilationO2,24h
1 1 0 0
2 1 0 0
3 1 0 0
4 1 0 0
5 1 0 0
6 1 0 0
7 1 0 0
8 1 0 0
9 1 1 0
10 1 1 0
11 1 1 0
12 1 1 0
13 1 1 0
14 1 1 0
15 1 1 0
16 1 1 0
17 1 1 0
18 1 0 1
19 1 0 1
20 1 0 1
21 1 0 1
22 1 0 1
attr(,"assign")
[1] 0 1 1
attr(,"contrasts")
attr(,"contrasts")$ventilation
[1] "contr.treatment"
> attach(fake.trypsin)
> summary(fake.trypsin)
trypsin grp grpf
Min. :-39.96 Min. :1.000 1: 32
1st Qu.:119.52 1st Qu.:2.000 2:137
Median :167.59 Median :2.000 3: 38
Mean :168.68 Mean :2.583 4: 44
3rd Qu.:213.98 3rd Qu.:3.000 5: 16
Max. :390.13 Max. :6.000 6: 4
> anova(lm(trypsin~grpf))
Analysis of Variance Table
Response: trypsin
Df Sum Sq Mean Sq F value Pr(>F)
grpf 5 224103 44821 13.508 9.592e-12 ***
Residuals 265 879272 3318
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> anova(lm(trypsin~grp))
Analysis of Variance Table
Response: trypsin
Df Sum Sq Mean Sq F value Pr(>F)
grp 1 206698 206698 62.009 8.451e-14 ***
Residuals 269 896677 3333
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> model1 <- lm(trypsin~grp)
> model2 <- lm(trypsin~grpf)
> anova(model1,model2)
Analysis of Variance Table
Model 1: trypsin ~ grp
Model 2: trypsin ~ grpf
Res.Df RSS Df Sum of Sq F Pr(>F)
1 269 896677
2 265 879272 4 17405 1.3114 0.2661
> anova(lm(trypsin~grp+grpf))
Analysis of Variance Table
Response: trypsin
Df Sum Sq Mean Sq F value Pr(>F)
grp 1 206698 206698 62.2959 7.833e-14 ***
grpf 4 17405 4351 1.3114 0.2661
Residuals 265 879272 3318
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> xbar.trypsin <- tapply(trypsin,grpf,mean)
> stripchart(trypsin~grp, method="jitter",
+ jitter=.1, vertical=T, pch=20)
> lines(1:6,xbar.trypsin,type="b",pch=4,cex=2,lty=2)
> abline(lm(trypsin~grp))
> if (.make.epsf) dev.copy2eps(file="trypsin.ps")
> n <- c(32,137, 38,44,16,4)
> tryp.mean <- c(128,152,194,207,215,218)
> tryp.sd <-c(50.9,58.5,49.3,66.3,60,14)
> gr<-1:6
> anova(lm(tryp.mean~gr+factor(gr),weights=n))
Analysis of Variance Table
Response: tryp.mean
Df Sum Sq Mean Sq F value Pr(>F)
gr 1 206698 206698 NaN NaN
factor(gr) 4 17405 4351 NaN NaN
Residuals 0 0 NaN
Warning message:
In anova.lm(lm(tryp.mean ~ gr + factor(gr), weights = n)) :
ANOVA F-tests on an essentially perfect fit are unreliable
> sum(tryp.sd^2*(n-1))
[1] 879271.9
> sum(n-1)
[1] 265
> sum(tryp.sd^2*(n-1))/sum(n-1)
[1] 3318.007
> 206698/3318.007 # F statistic for gr
[1] 62.29583
> 1-pf(206698/3318.007,1,265) # p-value
[1] 7.838175e-14
> 4351/3318.007 # F statistic for factor(gr)
[1] 1.311329
> 1-pf(4351/3318.007,4,265) # p-value
[1] 0.2660733
> attach(coking)
> anova(lm(time~width*temp))
Analysis of Variance Table
Response: time
Df Sum Sq Mean Sq F value Pr(>F)
width 2 123.143 61.572 222.102 3.312e-10 ***
temp 1 17.209 17.209 62.076 4.394e-06 ***
width:temp 2 5.701 2.851 10.283 0.002504 **
Residuals 12 3.327 0.277
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> tapply(time,list(width,temp),mean)
1600 1900
4 3.066667 2.300000
8 7.166667 5.533333
12 10.800000 7.333333
> hellung
glucose conc diameter
1 1 631000 21.2
2 1 592000 21.5
3 1 563000 21.3
4 1 475000 21.0
5 1 461000 21.5
6 1 416000 21.3
7 1 385000 20.3
8 1 321000 22.7
9 1 302000 21.5
10 1 199000 22.2
11 1 195000 22.6
12 1 165000 22.5
13 1 137000 23.3
14 1 130000 23.5
15 1 90000 23.4
16 1 66000 23.6
17 1 55000 23.9
18 1 52000 23.6
19 1 46000 23.6
20 1 41000 24.5
21 1 38000 24.0
22 1 34000 25.4
23 1 32000 24.6
24 1 30000 24.9
25 1 28000 24.9
26 1 21000 24.4
27 1 20000 25.9
28 1 16000 25.5
29 1 14500 25.7
30 1 13500 25.9
31 1 11600 25.7
32 1 11000 26.3
33 2 630000 19.2
34 2 501000 19.5
35 2 332000 19.8
36 2 285000 21.0
37 2 201000 21.0
38 2 175000 21.0
39 2 129000 21.3
40 2 111000 20.5
41 2 78000 22.6
42 2 70000 22.7
43 2 69000 22.2
44 2 62000 22.7
45 2 35000 24.0
46 2 27000 23.6
47 2 24000 23.5
48 2 22000 23.3
49 2 14000 24.4
50 2 13000 24.3
51 2 11000 24.2
> summary(hellung)
glucose conc diameter
Min. :1.000 Min. : 11000 Min. :19.20
1st Qu.:1.000 1st Qu.: 27500 1st Qu.:21.40
Median :1.000 Median : 69000 Median :23.30
Mean :1.373 Mean :164325 Mean :23.00
3rd Qu.:2.000 3rd Qu.:243000 3rd Qu.:24.35
Max. :2.000 Max. :631000 Max. :26.30
> hellung$glucose <- factor(hellung$glucose, labels=c("Yes","No"))
> summary(hellung)
glucose conc diameter
Yes:32 Min. : 11000 Min. :19.20
No :19 1st Qu.: 27500 1st Qu.:21.40
Median : 69000 Median :23.30
Mean :164325 Mean :23.00
3rd Qu.:243000 3rd Qu.:24.35
Max. :631000 Max. :26.30
> attach(hellung)
> plot(conc,diameter,pch=as.numeric(glucose))
> locator <- function(n)list(x=4e5,y=26)
> legend(locator(n=1),legend=c("glucose","no glucose"),pch=1:2)
> if (.make.epsf) dev.copy2eps(file="hellung-raw.ps")
> plot(conc,diameter,pch=as.numeric(glucose),log="x")
> if (.make.epsf) dev.copy2eps(file="hellung-logx.ps")
> plot(conc,diameter,pch=as.numeric(glucose),log="xy")
> tethym.gluc <- hellung[glucose=="Yes",]
> tethym.nogluc <- hellung[glucose=="No",]
> lm.nogluc <- lm(log10(diameter)~ log10(conc),data=tethym.nogluc)
> lm.gluc <- lm(log10(diameter)~ log10(conc),data=tethym.gluc)
> abline(lm.nogluc)
> abline(lm.gluc)
> if (.make.epsf) dev.copy2eps(file="hellung-loglog-lines.ps")
> summary(lm(log10(diameter)~ log10(conc), data=tethym.gluc))
Call:
lm(formula = log10(diameter) ~ log10(conc), data = tethym.gluc)
Residuals:
Min 1Q Median 3Q Max
-0.0267219 -0.0043361 0.0006891 0.0035489 0.0176077
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.63134 0.01345 121.29 <2e-16 ***
log10(conc) -0.05320 0.00272 -19.56 <2e-16 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.008779 on 30 degrees of freedom
Multiple R-squared: 0.9273, Adjusted R-squared: 0.9248
F-statistic: 382.5 on 1 and 30 DF, p-value: < 2.2e-16
> summary(lm(log10(diameter)~ log10(conc), data=tethym.nogluc))
Call:
lm(formula = log10(diameter) ~ log10(conc), data = tethym.nogluc)
Residuals:
Min 1Q Median 3Q Max
-0.021919 -0.004977 0.000056 0.005597 0.016625
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.634761 0.020209 80.89 < 2e-16 ***
log10(conc) -0.059677 0.004125 -14.47 5.48e-11 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.009532 on 17 degrees of freedom
Multiple R-squared: 0.9249, Adjusted R-squared: 0.9205
F-statistic: 209.3 on 1 and 17 DF, p-value: 5.482e-11
> summary(lm(log10(diameter)~log10(conc)*glucose))
Call:
lm(formula = log10(diameter) ~ log10(conc) * glucose)
Residuals:
Min 1Q Median 3Q Max
-0.026722 -0.004888 0.000056 0.003767 0.017608
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.631344 0.013879 117.543 <2e-16 ***
log10(conc) -0.053196 0.002807 -18.954 <2e-16 ***
glucoseNo 0.003418 0.023695 0.144 0.886
log10(conc):glucoseNo -0.006480 0.004821 -1.344 0.185
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.009059 on 47 degrees of freedom
Multiple R-squared: 0.9361, Adjusted R-squared: 0.9321
F-statistic: 229.6 on 3 and 47 DF, p-value: < 2.2e-16
> summary(lm(log10(diameter)~log10(conc)+glucose))
Call:
lm(formula = log10(diameter) ~ log10(conc) + glucose)
Residuals:
Min 1Q Median 3Q Max
-0.025243 -0.005733 -0.000195 0.004894 0.018914
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.642132 0.011417 143.83 < 2e-16 ***
log10(conc) -0.055393 0.002301 -24.07 < 2e-16 ***
glucoseNo -0.028238 0.002647 -10.67 2.93e-14 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.009134 on 48 degrees of freedom
Multiple R-squared: 0.9337, Adjusted R-squared: 0.9309
F-statistic: 337.9 on 2 and 48 DF, p-value: < 2.2e-16
> var.test(lm.gluc,lm.nogluc)
F test to compare two variances
data: lm.gluc and lm.nogluc
F = 0.84817, num df = 30, denom df = 17, p-value = 0.6731
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.3389901 1.9129940
sample estimates:
ratio of variances
0.8481674
> anova(lm(log10(diameter)~ log10(conc)*glucose))
Analysis of Variance Table
Response: log10(diameter)
Df Sum Sq Mean Sq F value Pr(>F)
log10(conc) 1 0.046890 0.046890 571.436 < 2.2e-16 ***
glucose 1 0.009494 0.009494 115.698 2.89e-14 ***
log10(conc):glucose 1 0.000148 0.000148 1.807 0.1853
Residuals 47 0.003857 0.000082
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> anova(lm(log10(diameter)~glucose+log10(conc)))
Analysis of Variance Table
Response: log10(diameter)
Df Sum Sq Mean Sq F value Pr(>F)
glucose 1 0.008033 0.008033 96.278 4.696e-13 ***
log10(conc) 1 0.048351 0.048351 579.494 < 2.2e-16 ***
Residuals 48 0.004005 0.000083
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> anova(lm(log10(diameter)~log10(conc)+ glucose))
Analysis of Variance Table
Response: log10(diameter)
Df Sum Sq Mean Sq F value Pr(>F)
log10(conc) 1 0.046890 0.046890 561.99 < 2.2e-16 ***
glucose 1 0.009494 0.009494 113.78 2.932e-14 ***
Residuals 48 0.004005 0.000083
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> t.test(log10(diameter)~glucose)
Welch Two Sample t-test
data: log10(diameter) by glucose
t = 2.7037, df = 36.31, p-value = 0.01037
alternative hypothesis: true difference in means between group Yes and group No is not equal to 0
95 percent confidence interval:
0.006492194 0.045424241
sample estimates:
mean in group Yes mean in group No
1.370046 1.344088
> attach(thuesen)
> options(na.action="na.exclude")
> lm.velo <- lm(short.velocity~blood.glucose)
> opar <- par(mfrow=c(2,2), mex=0.6, mar=c(4,4,3,2)+.3)
> plot(lm.velo, which=1:4)
> par(opar)
> if (.make.epsf) dev.copy2eps(file="regr-diag.ps")
> opar <- par(mfrow=c(2,2), mex=0.6, mar=c(4,4,3,2)+.3)
> plot(rstandard(lm.velo))
> plot(rstudent(lm.velo))
> plot(dffits(lm.velo),type="l")
> matplot(dfbetas(lm.velo),type="l", col="black")
> lines(sqrt(cooks.distance(lm.velo)), lwd=2)
> par(opar)
> if (.make.epsf) dev.copy2eps(file="regr-diag2.ps")
> summary(lm(short.velocity~blood.glucose, subset=-13))
Call:
lm(formula = short.velocity ~ blood.glucose, subset = -13)
Residuals:
Min 1Q Median 3Q Max
-0.31346 -0.11136 -0.01247 0.06043 0.40794
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.18929 0.11061 10.752 9.22e-10 ***
blood.glucose 0.01082 0.01029 1.052 0.305
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.193 on 20 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.05241, Adjusted R-squared: 0.005026
F-statistic: 1.106 on 1 and 20 DF, p-value: 0.3055
> cookd <- cooks.distance(lm(pemax~height+weight))
> cookd <- cookd/max(cookd)
> cook.colors <- gray(1-sqrt(cookd))
> plot(height,weight,bg=cook.colors,pch=21,cex=1.5)
> points(height,weight,pch=1,cex=1.5)
> if (.make.epsf) dev.copy2eps(file="cookd-cyst-fibr.ps")
> attach(secher)
> rst <- rstudent(lm(log10(bwt)~log10(ad)+log10(bpd)))
> range(rst)
[1] -3.707509 3.674050
> rst <- rst/3.71
> plot(ad,bpd,log="xy",bg=gray(1-abs(rst)),
+ pch=ifelse(rst>0,24,25), cex=1.5)
> if (.make.epsf) dev.copy2eps(file="rstudent-secher.ps")
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> no.yes <- c("No","Yes")
> smoking <- gl(2,1,8,no.yes)
> obesity <- gl(2,2,8,no.yes)
> snoring <- gl(2,4,8,no.yes)
> n.tot <- c(60,17,8,2,187,85,51,23)
> n.hyp <- c(5,2,1,0,35,13,15,8)
> data.frame(smoking,obesity,snoring,n.tot,n.hyp)
smoking obesity snoring n.tot n.hyp
1 No No No 60 5
2 Yes No No 17 2
3 No Yes No 8 1
4 Yes Yes No 2 0
5 No No Yes 187 35
6 Yes No Yes 85 13
7 No Yes Yes 51 15
8 Yes Yes Yes 23 8
> expand.grid(smoking=no.yes, obesity=no.yes, snoring=no.yes)
smoking obesity snoring
1 No No No
2 Yes No No
3 No Yes No
4 Yes Yes No
5 No No Yes
6 Yes No Yes
7 No Yes Yes
8 Yes Yes Yes
> hyp.tbl <- cbind(n.hyp,n.tot-n.hyp)
> hyp.tbl
n.hyp
[1,] 5 55
[2,] 2 15
[3,] 1 7
[4,] 0 2
[5,] 35 152
[6,] 13 72
[7,] 15 36
[8,] 8 15
> glm(hyp.tbl~smoking+obesity+snoring,family=binomial("logit"))
Call: glm(formula = hyp.tbl ~ smoking + obesity + snoring, family = binomial("logit"))
Coefficients:
(Intercept) smokingYes obesityYes snoringYes
-2.37766 -0.06777 0.69531 0.87194
Degrees of Freedom: 7 Total (i.e. Null); 4 Residual
Null Deviance: 14.13
Residual Deviance: 1.618 AIC: 34.54
> glm(hyp.tbl~smoking+obesity+snoring,binomial)
Call: glm(formula = hyp.tbl ~ smoking + obesity + snoring, family = binomial)
Coefficients:
(Intercept) smokingYes obesityYes snoringYes
-2.37766 -0.06777 0.69531 0.87194
Degrees of Freedom: 7 Total (i.e. Null); 4 Residual
Null Deviance: 14.13
Residual Deviance: 1.618 AIC: 34.54
> prop.hyp <- n.hyp/n.tot
> glm.hyp <- glm(prop.hyp~smoking+obesity+snoring,
+ binomial,weights=n.tot)
> glm(hyp.tbl~smoking+obesity+snoring, binomial("logit"))
Call: glm(formula = hyp.tbl ~ smoking + obesity + snoring, family = binomial("logit"))
Coefficients:
(Intercept) smokingYes obesityYes snoringYes
-2.37766 -0.06777 0.69531 0.87194
Degrees of Freedom: 7 Total (i.e. Null); 4 Residual
Null Deviance: 14.13
Residual Deviance: 1.618 AIC: 34.54
> glm.hyp <- glm(hyp.tbl~smoking+obesity+snoring,binomial)
> summary(glm.hyp)
Call:
glm(formula = hyp.tbl ~ smoking + obesity + snoring, family = binomial)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.37766 0.38018 -6.254 4e-10 ***
smokingYes -0.06777 0.27812 -0.244 0.8075
obesityYes 0.69531 0.28509 2.439 0.0147 *
snoringYes 0.87194 0.39757 2.193 0.0283 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 14.1259 on 7 degrees of freedom
Residual deviance: 1.6184 on 4 degrees of freedom
AIC: 34.537
Number of Fisher Scoring iterations: 4
> summary(glm(formula = hyp.tbl ~ smoking + obesity + snoring, family =
+ binomial))
Call:
glm(formula = hyp.tbl ~ smoking + obesity + snoring, family = binomial)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.37766 0.38018 -6.254 4e-10 ***
smokingYes -0.06777 0.27812 -0.244 0.8075
obesityYes 0.69531 0.28509 2.439 0.0147 *
snoringYes 0.87194 0.39757 2.193 0.0283 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 14.1259 on 7 degrees of freedom
Residual deviance: 1.6184 on 4 degrees of freedom
AIC: 34.537
Number of Fisher Scoring iterations: 4
> glm.hyp <- glm(hyp.tbl~obesity+snoring,binomial)
> summary(glm.hyp)
Call:
glm(formula = hyp.tbl ~ obesity + snoring, family = binomial)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.3921 0.3757 -6.366 1.94e-10 ***
obesityYes 0.6954 0.2851 2.440 0.0147 *
snoringYes 0.8655 0.3967 2.182 0.0291 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 14.1259 on 7 degrees of freedom
Residual deviance: 1.6781 on 5 degrees of freedom
AIC: 32.597
Number of Fisher Scoring iterations: 4
> glm.hyp <- glm(hyp.tbl~smoking+obesity+snoring,binomial)
> anova(glm.hyp, test="Chisq")
Analysis of Deviance Table
Model: binomial, link: logit
Response: hyp.tbl
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 7 14.1259
smoking 1 0.0022 6 14.1237 0.962724
obesity 1 6.8274 5 7.2963 0.008977 **
snoring 1 5.6779 4 1.6184 0.017179 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> glm.hyp <- glm(hyp.tbl~snoring+obesity+smoking,binomial)
> anova(glm.hyp, test="Chisq")
Analysis of Deviance Table
Model: binomial, link: logit
Response: hyp.tbl
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 7 14.1259
snoring 1 6.7887 6 7.3372 0.009174 **
obesity 1 5.6591 5 1.6781 0.017365 *
smoking 1 0.0597 4 1.6184 0.806938
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> glm.hyp <- glm(hyp.tbl~obesity+snoring,binomial)
> anova(glm.hyp, test="Chisq")
Analysis of Deviance Table
Model: binomial, link: logit
Response: hyp.tbl
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 7 14.1259
obesity 1 6.8260 6 7.2999 0.008984 **
snoring 1 5.6218 5 1.6781 0.017738 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> drop1(glm.hyp, test="Chisq")
Single term deletions
Model:
hyp.tbl ~ obesity + snoring
Df Deviance AIC LRT Pr(>Chi)
<none> 1.6781 32.597
obesity 1 7.3372 36.256 5.6591 0.01737 *
snoring 1 7.2999 36.219 5.6218 0.01774 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> caesar.shoe
<4 4 4.5 5 5.5 6+
Yes 5 7 6 7 8 10
No 17 28 36 41 46 140
> shoe.score <- 1:6
> shoe.score
[1] 1 2 3 4 5 6
> summary(glm(t(caesar.shoe)~shoe.score,binomial))
Call:
glm(formula = t(caesar.shoe) ~ shoe.score, family = binomial)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.87058 0.40506 -2.149 0.03161 *
shoe.score -0.25971 0.09361 -2.774 0.00553 **
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 9.3442 on 5 degrees of freedom
Residual deviance: 1.7845 on 4 degrees of freedom
AIC: 27.616
Number of Fisher Scoring iterations: 4
> anova(glm(t(caesar.shoe)~shoe.score,binomial))
Analysis of Deviance Table
Model: binomial, link: logit
Response: t(caesar.shoe)
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 5 9.3442
shoe.score 1 7.5597 4 1.7845 0.005969 **
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> caesar.shoe.yes <- caesar.shoe["Yes",]
> caesar.shoe.no <- caesar.shoe["No",]
> caesar.shoe.total <- caesar.shoe.yes+caesar.shoe.no
> prop.trend.test(caesar.shoe.yes,caesar.shoe.total)
Chi-squared Test for Trend in Proportions
data: caesar.shoe.yes out of caesar.shoe.total ,
using scores: 1 2 3 4 5 6
X-squared = 8.0237, df = 1, p-value = 0.004617
> prop.test(caesar.shoe.yes,caesar.shoe.total)
6-sample test for equality of proportions without
continuity correction
data: caesar.shoe.yes out of caesar.shoe.total
X-squared = 9.2874, df = 5, p-value = 0.09814
alternative hypothesis: two.sided
sample estimates:
prop 1 prop 2 prop 3 prop 4 prop 5 prop 6
0.22727273 0.20000000 0.14285714 0.14583333 0.14814815 0.06666667
Warning message:
In prop.test(caesar.shoe.yes, caesar.shoe.total) :
Chi-squared approximation may be incorrect
> confint(glm.hyp)
Waiting for profiling to be done...
2.5 % 97.5 %
(Intercept) -3.2101098 -1.718094
obesityYes 0.1254244 1.246789
snoringYes 0.1410076 1.715763
> confint.default(glm.hyp)
2.5 % 97.5 %
(Intercept) -3.12852108 -1.655631
obesityYes 0.13670388 1.254134
snoringYes 0.08801498 1.642902
> library(MASS)
> plot(profile(glm.hyp))
> if (.make.epsf) dev.copy2eps(file="profile-hyp.ps")
> exp(cbind(OR=coef(glm.hyp), confint(glm.hyp)))
Waiting for profiling to be done...
OR 2.5 % 97.5 %
(Intercept) 0.09143963 0.04035218 0.1794079
obesityYes 2.00454846 1.13362951 3.4791517
snoringYes 2.37609483 1.15143343 5.5609161
> juul$menarche <- factor(juul$menarche, labels=c("No","Yes"))
> juul$tanner <- factor(juul$tanner)
> juul.girl <- subset(juul,age>8 & age<20 &
+ complete.cases(menarche))
> attach(juul.girl)
The following object is masked from package:MASS:
menarche
> summary(glm(menarche~age,binomial))
Call:
glm(formula = menarche ~ age, family = binomial)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -20.0132 2.0284 -9.867 <2e-16 ***
age 1.5173 0.1544 9.829 <2e-16 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 719.39 on 518 degrees of freedom
Residual deviance: 200.66 on 517 degrees of freedom
AIC: 204.66
Number of Fisher Scoring iterations: 7
> summary(glm(menarche~age+tanner,binomial))
Call:
glm(formula = menarche ~ age + tanner, family = binomial)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -13.7758 2.7630 -4.986 6.17e-07 ***
age 0.8603 0.2311 3.723 0.000197 ***
tanner2 -0.5211 1.4846 -0.351 0.725609
tanner3 0.8264 1.2377 0.668 0.504313
tanner4 2.5645 1.2172 2.107 0.035132 *
tanner5 5.1897 1.4140 3.670 0.000242 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 604.19 on 435 degrees of freedom
Residual deviance: 106.60 on 430 degrees of freedom
(83 observations deleted due to missingness)
AIC: 118.6
Number of Fisher Scoring iterations: 8
> drop1(glm(menarche~age+tanner,binomial),test="Chisq")
Single term deletions
Model:
menarche ~ age + tanner
Df Deviance AIC LRT Pr(>Chi)
<none> 106.60 118.60
age 1 124.50 134.50 17.901 2.327e-05 ***
tanner 4 161.88 165.88 55.282 2.835e-11 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> predict(glm.hyp)
1 2 3 4 5 6
-2.3920763 -2.3920763 -1.6966575 -1.6966575 -1.5266180 -1.5266180
7 8
-0.8311991 -0.8311991
> predict(glm.hyp, type="response")
1 2 3 4 5 6
0.08377892 0.08377892 0.15490233 0.15490233 0.17848906 0.17848906
7 8
0.30339158 0.30339158
> glm.menarche <- glm(menarche~age, binomial)
> Age <- seq(8,20,.1)
> newages <- data.frame(age=Age)
> predicted.probability <- predict(glm.menarche,
+ newages,type="resp")
> plot(predicted.probability ~ Age, type="l")
> if (.make.epsf) dev.copy2eps(file="menarche-fit.ps")
> fitted(glm.hyp)
1 2 3 4 5 6
0.08377892 0.08377892 0.15490233 0.15490233 0.17848906 0.17848906
7 8
0.30339158 0.30339158
> prop.hyp
[1] 0.08333333 0.11764706 0.12500000 0.00000000 0.18716578
[6] 0.15294118 0.29411765 0.34782609
> fitted(glm.hyp)*n.tot
1 2 3 4 5 6
5.0267351 1.4242416 1.2392186 0.3098047 33.3774535 15.1715698
7 8
15.4729705 6.9780063
> data.frame(fit=fitted(glm.hyp)*n.tot,n.hyp,n.tot)
fit n.hyp n.tot
1 5.0267351 5 60
2 1.4242416 2 17
3 1.2392186 1 8
4 0.3098047 0 2
5 33.3774535 35 187
6 15.1715698 13 85
7 15.4729705 15 51
8 6.9780063 8 23
> age.group <- cut(age,c(8,10,12,13,14,15,16,18,20))
> tb <- table(age.group,menarche)
> tb
menarche
age.group No Yes
(8,10] 100 0
(10,12] 97 4
(12,13] 32 21
(13,14] 22 20
(14,15] 5 36
(15,16] 0 31
(16,18] 0 105
(18,20] 0 46
> rel.freq <- prop.table(tb,1)[,2]
> rel.freq
(8,10] (10,12] (12,13] (13,14] (14,15] (15,16]
0.00000000 0.03960396 0.39622642 0.47619048 0.87804878 1.00000000
(16,18] (18,20]
1.00000000 1.00000000
> points(rel.freq ~ c(9,11,12.5,13.5,14.5,15.5,17,19),pch=5)
> if (.make.epsf) dev.copy2eps(file="menarche-fit+obs.ps")
> age.gr <- cut(age,c(8,12,13,14,20))
> summary(glm(menarche~age+age.gr,binomial))
Call:
glm(formula = menarche ~ age + age.gr, family = binomial)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -21.5683 5.0645 -4.259 2.06e-05 ***
age 1.6250 0.4416 3.680 0.000233 ***
age.gr(12,13] 0.7296 0.7856 0.929 0.353024
age.gr(13,14] -0.5219 1.1184 -0.467 0.640765
age.gr(14,20] 0.2751 1.6065 0.171 0.864053
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 719.39 on 518 degrees of freedom
Residual deviance: 192.61 on 514 degrees of freedom
AIC: 202.61
Number of Fisher Scoring iterations: 8
> anova(glm(menarche~age+age.gr,binomial))
Analysis of Deviance Table
Model: binomial, link: logit
Response: menarche
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 518 719.39
age 1 518.73 517 200.66 < 2e-16 ***
age.gr 3 8.06 514 192.61 0.04483 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> 1-pchisq(8.058,3)
[1] 0.04482811
> anova(glm(menarche~age+I(age^2)+I(age^3)+age.gr,binomial))
Analysis of Deviance Table
Model: binomial, link: logit
Response: menarche
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 518 719.39
age 1 518.73 517 200.66 < 2.2e-16 ***
I(age^2) 1 0.05 516 200.61 0.819906
I(age^3) 1 8.82 515 191.80 0.002984 **
age.gr 3 3.34 512 188.46 0.342745
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Warning messages:
1: glm.fit: fitted probabilities numerically 0 or 1 occurred
2: glm.fit: fitted probabilities numerically 0 or 1 occurred
> glm.menarche <- glm(menarche~age+I(age^2)+I(age^3), binomial)
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
> predicted.probability <-
+ predict(glm.menarche, newages, type="resp")
> plot(predicted.probability ~ Age, type="l")
> points(rel.freq~c(9,11,12.5,13.5,14.5,15.5,17,19), pch=5)
> if (.make.epsf) dev.copy2eps(file="menarche-cubic.ps")
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> library(survival)
Attaching package: `survival'
The following object is masked from `package:ISwR':
lung
> attach(melanom)
> names(melanom)
[1] "no" "status" "days" "ulc" "thick" "sex"
> Surv(days, status==1)
[1] 10+ 30+ 35+ 99+ 185 204 210 232 232+ 279
[11] 295 355+ 386 426 469 493+ 529 621 629 659
[21] 667 718 752 779 793 817 826+ 833 858 869
[31] 872 967 977 982 1041 1055 1062 1075 1156 1228
[41] 1252 1271 1312 1427+ 1435 1499+ 1506 1508+ 1510+ 1512+
[51] 1516 1525+ 1542+ 1548 1557+ 1560 1563+ 1584 1605+ 1621
[61] 1627+ 1634+ 1641+ 1641+ 1648+ 1652+ 1654+ 1654+ 1667 1678+
[71] 1685+ 1690 1710+ 1710+ 1726 1745+ 1762+ 1779+ 1787+ 1787+
[81] 1793+ 1804+ 1812+ 1836+ 1839+ 1839+ 1854+ 1856+ 1860+ 1864+
[91] 1899+ 1914+ 1919+ 1920+ 1927+ 1933 1942+ 1955+ 1956+ 1958+
[101] 1963+ 1970+ 2005+ 2007+ 2011+ 2024+ 2028+ 2038+ 2056+ 2059+
[111] 2061 2062 2075+ 2085+ 2102+ 2103 2104+ 2108 2112+ 2150+
[121] 2156+ 2165+ 2209+ 2227+ 2227+ 2256 2264+ 2339+ 2361+ 2387+
[131] 2388 2403+ 2426+ 2426+ 2431+ 2460+ 2467 2492+ 2493+ 2521+
[141] 2542+ 2559+ 2565 2570+ 2660+ 2666+ 2676+ 2738+ 2782 2787+
[151] 2984+ 3032+ 3040+ 3042 3067+ 3079+ 3101+ 3144+ 3152+ 3154+
[161] 3180+ 3182+ 3185+ 3199+ 3228+ 3229+ 3278+ 3297+ 3328+ 3330+
[171] 3338 3383+ 3384+ 3385+ 3388+ 3402+ 3441+ 3458+ 3459+ 3459+
[181] 3476+ 3523+ 3667+ 3695+ 3695+ 3776+ 3776+ 3830+ 3856+ 3872+
[191] 3909+ 3968+ 4001+ 4103+ 4119+ 4124+ 4207+ 4310+ 4390+ 4479+
[201] 4492+ 4668+ 4688+ 4926+ 5565+
> survfit(Surv(days,status==1)~1)
Call: survfit(formula = Surv(days, status == 1) ~ 1)
n events median 0.95LCL 0.95UCL
[1,] 205 57 NA NA NA
> surv.all <- survfit(Surv(days,status==1)~1)
> summary(surv.all)
Call: survfit(formula = Surv(days, status == 1) ~ 1)
time n.risk n.event survival std.err lower 95% CI upper 95% CI
185 201 1 0.995 0.00496 0.985 1.000
204 200 1 0.990 0.00700 0.976 1.000
210 199 1 0.985 0.00855 0.968 1.000
232 198 1 0.980 0.00985 0.961 1.000
279 196 1 0.975 0.01100 0.954 0.997
295 195 1 0.970 0.01202 0.947 0.994
386 193 1 0.965 0.01297 0.940 0.991
426 192 1 0.960 0.01384 0.933 0.988
469 191 1 0.955 0.01465 0.927 0.984
529 189 1 0.950 0.01542 0.920 0.981
621 188 1 0.945 0.01615 0.914 0.977
629 187 1 0.940 0.01683 0.907 0.973
659 186 1 0.935 0.01748 0.901 0.970
667 185 1 0.930 0.01811 0.895 0.966
718 184 1 0.925 0.01870 0.889 0.962
752 183 1 0.920 0.01927 0.883 0.958
779 182 1 0.915 0.01981 0.877 0.954
793 181 1 0.910 0.02034 0.871 0.950
817 180 1 0.904 0.02084 0.865 0.946
833 178 1 0.899 0.02134 0.859 0.942
858 177 1 0.894 0.02181 0.853 0.938
869 176 1 0.889 0.02227 0.847 0.934
872 175 1 0.884 0.02272 0.841 0.930
967 174 1 0.879 0.02315 0.835 0.926
977 173 1 0.874 0.02357 0.829 0.921
982 172 1 0.869 0.02397 0.823 0.917
1041 171 1 0.864 0.02436 0.817 0.913
1055 170 1 0.859 0.02474 0.812 0.909
1062 169 1 0.854 0.02511 0.806 0.904
1075 168 1 0.849 0.02547 0.800 0.900
1156 167 1 0.844 0.02582 0.794 0.896
1228 166 1 0.838 0.02616 0.789 0.891
1252 165 1 0.833 0.02649 0.783 0.887
1271 164 1 0.828 0.02681 0.777 0.883
1312 163 1 0.823 0.02713 0.772 0.878
1435 161 1 0.818 0.02744 0.766 0.874
1506 159 1 0.813 0.02774 0.760 0.869
1516 155 1 0.808 0.02805 0.755 0.865
1548 152 1 0.802 0.02837 0.749 0.860
1560 150 1 0.797 0.02868 0.743 0.855
1584 148 1 0.792 0.02899 0.737 0.851
1621 146 1 0.786 0.02929 0.731 0.846
1667 137 1 0.780 0.02963 0.725 0.841
1690 134 1 0.775 0.02998 0.718 0.836
1726 131 1 0.769 0.03033 0.712 0.831
1933 110 1 0.762 0.03085 0.704 0.825
2061 95 1 0.754 0.03155 0.694 0.818
2062 94 1 0.746 0.03221 0.685 0.812
2103 90 1 0.737 0.03290 0.676 0.805
2108 88 1 0.729 0.03358 0.666 0.798
2256 80 1 0.720 0.03438 0.656 0.791
2388 75 1 0.710 0.03523 0.645 0.783
2467 69 1 0.700 0.03619 0.633 0.775
2565 63 1 0.689 0.03729 0.620 0.766
2782 57 1 0.677 0.03854 0.605 0.757
3042 52 1 0.664 0.03994 0.590 0.747
3338 35 1 0.645 0.04307 0.566 0.735
> plot(surv.all)
> if (.make.epsf) dev.copy2eps(file="surv-all.ps")
> surv.bysex <- survfit(Surv(days,status==1)~sex)
> plot(surv.bysex)
> if (.make.epsf) dev.copy2eps(file="surv-bysex.ps")
> plot(surv.bysex, conf.int=T, col=c("black","gray"))
> survdiff(Surv(days,status==1)~sex)
Call:
survdiff(formula = Surv(days, status == 1) ~ sex)
N Observed Expected (O-E)^2/E (O-E)^2/V
sex=1 126 28 37.1 2.25 6.47
sex=2 79 29 19.9 4.21 6.47
Chisq= 6.5 on 1 degrees of freedom, p= 0.01
> survdiff(Surv(days,status==1)~sex+strata(ulc))
Call:
survdiff(formula = Surv(days, status == 1) ~ sex + strata(ulc))
N Observed Expected (O-E)^2/E (O-E)^2/V
sex=1 126 28 34.7 1.28 3.31
sex=2 79 29 22.3 1.99 3.31
Chisq= 3.3 on 1 degrees of freedom, p= 0.07
> summary(coxph(Surv(days,status==1)~sex))
Call:
coxph(formula = Surv(days, status == 1) ~ sex)
n= 205, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|)
sex 0.6622 1.9390 0.2651 2.498 0.0125 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
exp(coef) exp(-coef) lower .95 upper .95
sex 1.939 0.5157 1.153 3.26
Concordance= 0.59 (se = 0.034 )
Likelihood ratio test= 6.15 on 1 df, p=0.01
Wald test = 6.24 on 1 df, p=0.01
Score (logrank) test = 6.47 on 1 df, p=0.01
> summary(coxph(Surv(days,status==1)~sex+log(thick)+strata(ulc)))
Call:
coxph(formula = Surv(days, status == 1) ~ sex + log(thick) +
strata(ulc))
n= 205, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|)
sex 0.3600 1.4333 0.2702 1.332 0.1828
log(thick) 0.5599 1.7505 0.1784 3.139 0.0017 **
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
exp(coef) exp(-coef) lower .95 upper .95
sex 1.433 0.6977 0.844 2.434
log(thick) 1.750 0.5713 1.234 2.483
Concordance= 0.673 (se = 0.039 )
Likelihood ratio test= 13.3 on 2 df, p=0.001
Wald test = 12.88 on 2 df, p=0.002
Score (logrank) test = 12.98 on 2 df, p=0.002
> plot(survfit(coxph(Surv(days,status==1)~
+ log(thick)+sex+strata(ulc))))
Error in a$specials[[i]] : subscript out of bounds
Calls: plot ... survfit.coxph -> model.matrix.coxph -> delete.response -> off
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 2.0-12
Check: tests
Result: NOTE
Running ‘allexercises.R’
Comparing ‘allexercises.Rout’ to ‘allexercises.Rout.save’ ...
180c180
< V = 13858, p-value = 4.225e-14
---
> V = 9283.5, p-value = 2.075e-13
471c471
< V = 0, p-value = 0.0452
---
> V = 0, p-value = 0.05906
Running ‘allscripts.R’
Comparing ‘allscripts.Rout’ to ‘allscripts.Rout.save’ ... OK
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 2.0-12
Check: tests
Result: NOTE
Running 'allexercises.R' [5s]
Comparing 'allexercises.Rout' to 'allexercises.Rout.save' ...
180c180
< V = 13858, p-value = 4.225e-14
---
> V = 9283.5, p-value = 2.075e-13
471c471
< V = 0, p-value = 0.0452
---
> V = 0, p-value = 0.05906
Running 'allscripts.R' [5s]
Comparing 'allscripts.Rout' to 'allscripts.Rout.save' ... OK
Flavor: r-devel-windows-x86_64
Version: 2.0-12
Check: tests
Result: NOTE
Running ‘allexercises.R’ [1s/1s]
Comparing ‘allexercises.Rout’ to ‘allexercises.Rout.save’ ...26c26
< NA's :5 NA's :11 NA's :5 NA's :119 NA's :66
---
> NAs :5 NAs :11 NAs :5 NAs :119 NAs :66
34c34
< NA's :351
---
> NAs :351
Running ‘allscripts.R’ [1s/1s]
Comparing ‘allscripts.Rout’ to ‘allscripts.Rout.save’ ...597c597
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
607c607
< NA's :5 NA's :635 NA's :5 NA's :321
---
> NAs :5 NAs :635 NAs :5 NAs :321
615c615
< NA's :240 NA's :859
---
> NAs :240 NAs :859
623,638c623,638
< age menarche sex igf1
< Min. : 0.170 No :369 M :621 Min. : 25.0
< 1st Qu.: 9.053 Yes :335 F :713 1st Qu.:202.2
< Median :12.560 NA's:635 NA's: 5 Median :313.5
< Mean :15.095 Mean :340.2
< 3rd Qu.:16.855 3rd Qu.:462.8
< Max. :83.000 Max. :915.0
< NA's :5 NA's :321
< tanner testvol
< I :515 Min. : 1.000
< II :103 1st Qu.: 1.000
< III : 72 Median : 3.000
< IV : 81 Mean : 7.896
< V :328 3rd Qu.:15.000
< NA's:240 Max. :30.000
< NA's :859
---
> age menarche sex igf1 tanner
> Min. : 0.170 No :369 M :621 Min. : 25.0 I :515
> 1st Qu.: 9.053 Yes:335 F :713 1st Qu.:202.2 II :103
> Median :12.560 NAs:635 NAs: 5 Median :313.5 III: 72
> Mean :15.095 Mean :340.2 IV : 81
> 3rd Qu.:16.855 3rd Qu.:462.8 V :328
> Max. :83.000 Max. :915.0 NAs:240
> NAs :5 NAs :321
> testvol
> Min. : 1.000
> 1st Qu.: 1.000
> Median : 3.000
> Mean : 7.896
> 3rd Qu.:15.000
> Max. :30.000
> NAs :859
695c695
< Median :12.38 NA's:621 Median :280.0 III : 34
---
> Median :12.38 NAs:621 Median :280.0 III: 34
698,699c698,699
< Max. :83.00 Max. :915.0 NA's: 76
< NA's :145
---
> Max. :83.00 Max. :915.0 NAs: 76
> NAs :145
707c707
< NA's :141
---
> NAs :141
713c713
< Median :12.80 NA's: 9 Median :352.0 III : 38
---
> Median :12.80 NAs: 9 Median :352.0 III: 38
716,717c716,717
< Max. :75.12 Max. :914.0 NA's:159
< NA's :176
---
> Max. :75.12 Max. :914.0 NAs:159
> NAs :176
725c725
< NA's :713
---
> NAs :713
1279c1279
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1293c1293
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1329c1329
< I II III IV V NA's
---
> I II III IV V NAs
1798c1798
< Max. NA's
---
> Max. NAs
1806c1806
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
Flavor: r-oldrel-macos-arm64
Version: 2.0-12
Check: tests
Result: NOTE
Running ‘allexercises.R’ [3s/4s]
Comparing ‘allexercises.Rout’ to ‘allexercises.Rout.save’ ...26c26
< NA's :5 NA's :11 NA's :5 NA's :119 NA's :66
---
> NAs :5 NAs :11 NAs :5 NAs :119 NAs :66
34c34
< NA's :351
---
> NAs :351
Running ‘allscripts.R’ [3s/4s]
Comparing ‘allscripts.Rout’ to ‘allscripts.Rout.save’ ...597c597
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
607c607
< NA's :5 NA's :635 NA's :5 NA's :321
---
> NAs :5 NAs :635 NAs :5 NAs :321
615c615
< NA's :240 NA's :859
---
> NAs :240 NAs :859
623,638c623,638
< age menarche sex igf1
< Min. : 0.170 No :369 M :621 Min. : 25.0
< 1st Qu.: 9.053 Yes :335 F :713 1st Qu.:202.2
< Median :12.560 NA's:635 NA's: 5 Median :313.5
< Mean :15.095 Mean :340.2
< 3rd Qu.:16.855 3rd Qu.:462.8
< Max. :83.000 Max. :915.0
< NA's :5 NA's :321
< tanner testvol
< I :515 Min. : 1.000
< II :103 1st Qu.: 1.000
< III : 72 Median : 3.000
< IV : 81 Mean : 7.896
< V :328 3rd Qu.:15.000
< NA's:240 Max. :30.000
< NA's :859
---
> age menarche sex igf1 tanner
> Min. : 0.170 No :369 M :621 Min. : 25.0 I :515
> 1st Qu.: 9.053 Yes:335 F :713 1st Qu.:202.2 II :103
> Median :12.560 NAs:635 NAs: 5 Median :313.5 III: 72
> Mean :15.095 Mean :340.2 IV : 81
> 3rd Qu.:16.855 3rd Qu.:462.8 V :328
> Max. :83.000 Max. :915.0 NAs:240
> NAs :5 NAs :321
> testvol
> Min. : 1.000
> 1st Qu.: 1.000
> Median : 3.000
> Mean : 7.896
> 3rd Qu.:15.000
> Max. :30.000
> NAs :859
695c695
< Median :12.38 NA's:621 Median :280.0 III : 34
---
> Median :12.38 NAs:621 Median :280.0 III: 34
698,699c698,699
< Max. :83.00 Max. :915.0 NA's: 76
< NA's :145
---
> Max. :83.00 Max. :915.0 NAs: 76
> NAs :145
707c707
< NA's :141
---
> NAs :141
713c713
< Median :12.80 NA's: 9 Median :352.0 III : 38
---
> Median :12.80 NAs: 9 Median :352.0 III: 38
716,717c716,717
< Max. :75.12 Max. :914.0 NA's:159
< NA's :176
---
> Max. :75.12 Max. :914.0 NAs:159
> NAs :176
725c725
< NA's :713
---
> NAs :713
1279c1279
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1293c1293
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1329c1329
< I II III IV V NA's
---
> I II III IV V NAs
1798c1798
< Max. NA's
---
> Max. NAs
1806c1806
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
Flavor: r-oldrel-macos-x86_64
Version: 2.0-12
Check: tests
Result: NOTE
Running 'allexercises.R' [6s]
Comparing 'allexercises.Rout' to 'allexercises.Rout.save' ...26c26
< NA's :5 NA's :11 NA's :5 NA's :119 NA's :66
---
> NAs :5 NAs :11 NAs :5 NAs :119 NAs :66
34c34
< NA's :351
---
> NAs :351
Running 'allscripts.R' [6s]
Comparing 'allscripts.Rout' to 'allscripts.Rout.save' ...597c597
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
607c607
< NA's :5 NA's :635 NA's :5 NA's :321
---
> NAs :5 NAs :635 NAs :5 NAs :321
615c615
< NA's :240 NA's :859
---
> NAs :240 NAs :859
623,638c623,638
< age menarche sex igf1
< Min. : 0.170 No :369 M :621 Min. : 25.0
< 1st Qu.: 9.053 Yes :335 F :713 1st Qu.:202.2
< Median :12.560 NA's:635 NA's: 5 Median :313.5
< Mean :15.095 Mean :340.2
< 3rd Qu.:16.855 3rd Qu.:462.8
< Max. :83.000 Max. :915.0
< NA's :5 NA's :321
< tanner testvol
< I :515 Min. : 1.000
< II :103 1st Qu.: 1.000
< III : 72 Median : 3.000
< IV : 81 Mean : 7.896
< V :328 3rd Qu.:15.000
< NA's:240 Max. :30.000
< NA's :859
---
> age menarche sex igf1 tanner
> Min. : 0.170 No :369 M :621 Min. : 25.0 I :515
> 1st Qu.: 9.053 Yes:335 F :713 1st Qu.:202.2 II :103
> Median :12.560 NAs:635 NAs: 5 Median :313.5 III: 72
> Mean :15.095 Mean :340.2 IV : 81
> 3rd Qu.:16.855 3rd Qu.:462.8 V :328
> Max. :83.000 Max. :915.0 NAs:240
> NAs :5 NAs :321
> testvol
> Min. : 1.000
> 1st Qu.: 1.000
> Median : 3.000
> Mean : 7.896
> 3rd Qu.:15.000
> Max. :30.000
> NAs :859
695c695
< Median :12.38 NA's:621 Median :280.0 III : 34
---
> Median :12.38 NAs:621 Median :280.0 III: 34
698,699c698,699
< Max. :83.00 Max. :915.0 NA's: 76
< NA's :145
---
> Max. :83.00 Max. :915.0 NAs: 76
> NAs :145
707c707
< NA's :141
---
> NAs :141
713c713
< Median :12.80 NA's: 9 Median :352.0 III : 38
---
> Median :12.80 NAs: 9 Median :352.0 III: 38
716,717c716,717
< Max. :75.12 Max. :914.0 NA's:159
< NA's :176
---
> Max. :75.12 Max. :914.0 NAs:159
> NAs :176
725c725
< NA's :713
---
> NAs :713
1279c1279
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1293c1293
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1329c1329
< I II III IV V NA's
---
> I II III IV V NAs
1798c1798
< Max. NA's
---
> Max. NAs
1806c1806
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
Flavor: r-oldrel-windows-x86_64