... But of course the p-values are essentially meaningless without
some sort of multiplicity adjustment.
(search on "multiplicity adjustment" for details). :-(
-- Bert
On Mon, Aug 22, 2022 at 8:59 AM Ebert,Timothy Aaron <tebert at ufl.edu>
wrote:>
> A somewhat clunky solution:
> for(i in colnames(dat)){
> print(cor.test(dat[,i], dat$x1, method = "pearson", use =
"complete.obs")$estimate)
> print(cor.test(dat[,i], dat$x1, method = "pearson", use =
"complete.obs")$p.value)
> }
>
> Rather than printing you could set up an array or list to save the results.
>
>
> Tim
>
> -----Original Message-----
> From: R-help <r-help-bounces at r-project.org> On Behalf Of Val
> Sent: Monday, August 22, 2022 11:09 AM
> To: r-help at R-project.org (r-help at r-project.org) <r-help at
r-project.org>
> Subject: [R] Correlate
>
> [External Email]
>
> Hi all,
>
> I have a data set with ~250 variables(columns). I want to calculate the
correlation of one variable with the rest of the other variables and also want
the p-values for each correlation. Please see the sample data and my attempt.
I have got the correlation but unable to get the p-values
>
> dat <- read.table(text="x1 x2 x3 x4
> 1.68 -0.96 -1.25 0.61
> -0.06 0.41 0.06 -0.96
> . 0.08 1.14 1.42
> 0.80 -0.67 0.53 -0.68
> 0.23 -0.97 -1.18 -0.78
> -1.03 1.11 -0.61 .
> 2.15 . 0.02 0.66
> 0.35 -0.37 -0.26 0.39
> -0.66 0.89 . -1.49
> 0.11 1.52 0.73 -1.03",header=TRUE)
>
> #change all to numeric
> dat[] <- lapply(dat, function(x) as.numeric(as.character(x)))
>
> data_cor <- cor(dat[ , colnames(dat) != "x1"], dat$x1,
method = "pearson", use = "complete.obs")
>
> Result
> [,1]
> x2 -0.5845835
> x3 -0.4664220
> x4 0.7202837
>
> How do I get the p-values ?
>
> Thank you,
>
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