# [R] Correlate

Bert Gunter bgunter@4567 @end|ng |rom gm@||@com
Mon Aug 22 18:06:34 CEST 2022

```... But of course the p-values are essentially meaningless without
(search on "multiplicity adjustment" for details). :-(

-- Bert

On Mon, Aug 22, 2022 at 8:59 AM Ebert,Timothy Aaron <tebert using 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 using r-project.org> On Behalf Of Val
> Sent: Monday, August 22, 2022 11:09 AM
> To: r-help using R-project.org (r-help using r-project.org) <r-help using 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
>
> #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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