[R] A possible too old question on significant test of correlation matrix

[Guo Wei-Wei]

Hi, Gavin, your program is excellent. Thank  you very much!

And I have two further questions.

1. Since it is very possible that the data contains missing value and
the program will failed against missing values, I have to delete all
the cases contained NA. Can it be done pairwisely?
2. Can the program show t values instead of p values?

Best regards,
Wei-Wei

2006/7/10, Gavin Simpson <gavin.simpson at ucl.ac.uk>:
> On Mon, 2006-07-10 at 13:27 +0800, Guo Wei-Wei wrote:
> > Dear all,
> >
> > I'm working on a data.frame named en.data, which has n cases and m columns.
> > I generate the correlation matrix of en.data by
> >
> > > cor(en.data)
> >
> > I find that there is no p-value on each correlation in the correlation
> > matrix. I searched in the R-help mail list and found some related
> > posts, but I didn't find direct way to solve the problem. Someone said
> > to use cor.test() or t.test(). The problem is that cor.test() and
> > t.test() can only apply on two vectors, not on a data.frame or a
> > matrix.
> >
> > My solution is
> >
> > for (i in 1:(ncol(en.data) -1)) {
> >    cor.test(en.data[,i], en.data[, i+1])
> > }
> >
> > I think it is a stupid way. Is there a direct way to do so? After all,
> > it is a basic function to generate significant level of a correlation
> > in a correlation matrix.
> >
> > Thank you in advance!
> > Wei-Wei
>
> Hi,
>
> Bill Venables posted a solution to this on the R-Help list in Jan 2000.
> I made a minor modification to add a class to the result and wrote a
> print method (which could probably do with some tidying but it works).
>
> E.g.:
>
> # paste in the functions below, then
> data(iris)
> corProb(iris[,1:4])
>
> ## prints
> Correlations are shown below the diagonal
> P-values are shown above the diagonal
>
>              Sepal.Length Sepal.Width Petal.Length Petal.Width
> Sepal.Length  1.0000       0.1519      0.0000       0.0000
> Sepal.Width  -0.1176       1.0000      0.0000       0.0000
> Petal.Length  0.8718      -0.4284      1.0000       0.0000
> Petal.Width   0.8179      -0.3661      0.9629       1.0000
>
> Is this what you want?
>
> HTH
>
> G
>
> # correlation function
> # based on post by Bill Venables on R-Help
> # Date: Tue, 04 Jan 2000 15:05:39 +1000
> # https://stat.ethz.ch/pipermail/r-help/2000-January/009758.html
> # modified by G L Simpson, September 2003
> # version 0.2: added print.cor.prob
> #              added class statement to cor.prob
> # version 0.1: original function of Bill Venables
> corProb <- function(X, dfr = nrow(X) - 2) {
>     R <- cor(X)
>     above <- row(R) < col(R)
>     r2 <- R[above]^2
>     Fstat <- r2 * dfr / (1 - r2)
>     R[above] <- 1 - pf(Fstat, 1, dfr)
>     class(R) <- "corProb"
>     R
> }
> print.corProb <- function(x, digits = getOption("digits"), quote = FALSE, na.print = "",
>     justify = "none", ...) {
>     xx <- format(unclass(round(x, digits = 4)), digits = digits, justify = justify)
>     if (any(ina <- is.na(x)))
>         xx[ina] <- na.print
>     cat("\nCorrelations are shown below the diagonal\n")
>     cat("P-values are shown above the diagonal\n\n")
>     print(xx, quote = quote, ...)
>     invisible(x)
> }
>
> --
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> ECRC                              [f] +44 (0)20 7679 0565
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