[BioC] Linear Models and ANOVA
Thomas Hampton
thomas.h.hampton at dartmouth.edu
Thu Dec 16 22:03:58 CET 2010
This is an off topic question related more to R and statistics, but I
will impose myself, if you don't mind.
Here is my issue.
R anova is essentially a way to interpret some linear model such as
fit <- lm(y ~a*b)
You can generate nice p values by doing something like
anova(lm(y ~a*b))
But you could also generate p values like this:
summary(lm(y~a*b))
I find though, that the p values you generate may be different
depending on whether you call summary.lm or whether
you get them from anova.lm.
For example:
> data.ex2=read.table(datafilename,header=TRUE)
> summary(lm(formula = Alertness ~ Gender * Dosage, data = data.ex2))
Call:
lm(formula = Alertness ~ Gender * Dosage, data = data.ex2)
Residuals:
Min 1Q Median 3Q Max
-6.500 -3.375 0.000 1.562 10.500
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.750 2.546 6.185 4.69e-05 ***
Genderm -4.500 3.601 -1.250 0.235
Dosageb 1.000 3.601 0.278 0.786
Genderm:Dosageb 0.250 5.093 0.049 0.962
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.093 on 12 degrees of freedom
Multiple R-squared: 0.2079, Adjusted R-squared: 0.009862
F-statistic: 1.05 on 3 and 12 DF, p-value: 0.4062
> anova(lm(formula = Alertness ~ Gender * Dosage, data = data.ex2))
Analysis of Variance Table
Response: Alertness
Df Sum Sq Mean Sq F value Pr(>F)
Gender 1 76.562 76.562 2.9518 0.1115
Dosage 1 5.062 5.062 0.1952 0.6665
Gender:Dosage 1 0.063 0.063 0.0024 0.9617
Residuals 12 311.250 25.938
The anova output is tidier to look at. But why are the anova p values
smaller
for Gender and Dosage?
Thanks for your help.
Tom
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