On Sep 18, 2011, at 9:36 PM, Donald Price wrote:
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).
This sounds pretty suspicious. Is this plan well thought out from a
statistical viewpoint?
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
Where did you copy this code from?
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)
Have you looked at str(regressionresults[[1]])? It does not look to be
something that can be immediately accessed as though it were a matrix.
Perhaps after you extract the "coefficients" element.
5) regpvalues <- data.frame(lapply(regressionresults,
function(x){x["Pr(>|t|)"][1:2,]}))
I would have guessed:
regpvalues <- data.frame(lapply(regressionresults,
function(x){ x[["coefficients"]][1:2, "Pr(>|t|)"] }
The coefficient values are the rows, and the "Pr(>|t|)"'s are the
columns.
6) write.table( t(regpvalues), file = "regression-
resultsheadshape.txt",
quote = F, sep ='\t')
I doubt that t(.) will succeed. I'm not aware that "t" has a list
method
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
David Winsemius, MD
West Hartford, CT
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