Hi All,

I have run many regression analyses (14000 +) and want to collect the
coefficients and pvalues into an excel file.  I can get the statements below
to work up to step 4.  I can printout the regressionresults (sample output
below).

So my hope is to run something like step 5 and 6 and put the pvalues (and
then coefficients) into an excel file.  Can anyone suggest what I am doing
wrong or a better way :0

Thanks

Don





1) sdata <- read.table("gene.csv", row.names=1, sep=',')

2)  headshape <- c(0.575818, 0.573874, 0.525701, 0.548490, 0.685111,
0.592502, 0.566001, 0.563605, 0.637906, 0.578099, 0.588142, 0.383393,
0.561732, 0.456134, 0.430472, 0.603143, 0.514315, 0.53328, 0.482734,
0.637906)

3) morphtrait <- headshape aof<-function(x){m<-data.frame(morphtrait,x);
summary(lm(morphtrait~x, m))}

4) regressionresults <-apply(sdata, 1, aof)

5) regpvalues <- data.frame(lapply(regressionresults,
function(x){x["Pr(>|t|)"][1:2,]}))

6) write.table(t(regpvalues), file = "regression-resultsheadshape.txt",
quote = F, sep ='\t')





> regressionresults

$CUST_54_PI410671829

Call:
lm(formula = morphtrait ~ x, data = m)

Residuals:
     Min       1Q   Median       3Q      Max
-0.23217 -0.08980 -0.04592 -0.00947  1.07688

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5985797  0.0364510  16.421   <2e-16 ***
x           0.0005372  0.0011161   0.481    0.632
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2379 on 76 degrees of freedom
Multiple R-squared: 0.003039,   Adjusted R-squared: -0.01008
F-statistic: 0.2316 on 1 and 76 DF,  p-value: 0.6317


$CUST_13662_PI410671829

Call:
lm(formula = morphtrait ~ x, data = m)

Residuals:
     Min       1Q   Median       3Q      Max
-0.23434 -0.09000 -0.05062 -0.01427  1.06208

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.626368   0.030173  20.759   <2e-16 ***
x           -0.003815   0.003337  -1.143    0.256
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2362 on 76 degrees of freedom
Multiple R-squared: 0.01691,    Adjusted R-squared: 0.003976
F-statistic: 1.307 on 1 and 76 DF,  p-value: 0.2565


$CUST_8938_PI410671829

Call:
lm(formula = morphtrait ~ x, data = m)

Residuals:
     Min       1Q   Median       3Q      Max
-0.23424 -0.09051 -0.04721 -0.01216  1.07195

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6056038  0.0368423  16.438   <2e-16 ***
x           0.0004091  0.0021406   0.191    0.849
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2382 on 76 degrees of freedom
Multiple R-squared: 0.0004803,  Adjusted R-squared: -0.01267
F-statistic: 0.03652 on 1 and 76 DF,  p-value: 0.849


$CUST_5773_PI410671829

Call:
lm(formula = morphtrait ~ x, data = m)

Residuals:
     Min       1Q   Median       3Q      Max
-0.23587 -0.09139 -0.04895 -0.01037  1.07437

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.598110   0.038504  15.534   <2e-16 ***
x           0.001818   0.004070   0.447    0.656
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2379 on 76 degrees of freedom
Multiple R-squared: 0.002619,   Adjusted R-squared: -0.0105
F-statistic: 0.1996 on 1 and 76 DF,  p-value: 0.6563

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