[R-sig-eco] Use cor.test for multiple parameters?

[Martin Weiser]

Hello Kathleen,
disclaimer: here I doubt Your approach, so if You are really sure what
You want, do not take this mail into account. Otherwise: 
I know nothing about your mental abilities, but so many pairwise
correlations are about order of magnitude more than I am able to handle
with mine. For me, this is a good reason to use some multivariate method
(e.g. ordination) and make things much more simple. Then test just what
seems to be meaningful (and correct for non-independence) if You really
need it. This way Your tests could be viewed as post-hoc ("325
tests"-way is rigorous, but I am not sure whether it is useful).  Or
forget testing, and just look at the matrix of correlation coefficients
themselves - these numbers tell the story, p-values just say how likely
are they lying in doing so.
Best,
Martin Weiser
 
Wayne Richter píše v Pá 16. 11. 2012 v 12:59 -0500:
> Take a look at rcorr() in the Hmisc package. You can divide the P value matrix by the value needed for the Bonferroni correction.
> 
> But, with 26 parameters, you will have 325 pairwise correlations. By dividing, for example, an alpha of 0.05 by 325, your minimum P value for significance would be 0.00015. It's going to take a mighty high correlation coefficient to be significant with 60 observations. Take a look at p.adjust.
> 
> >>> 
> From: 	Kathleen Regan <kath.regan at gmail.com>
> To:	<r-sig-ecology at r-project.org>
> Date: 	11/15/2012 8:16 AM
> Subject: 	[R-sig-eco] Use cor.test for multiple parameters?
> 
> Please forgive me if this question has been answered somewhere else, but
> for the life of me I couldn't find an answer (although i have found that
> others have asked similar questions.)
> 
> If anyone replies, PLEASE keep in mind that I am a soil ecologist with
> absolutely no background or experience in object-oriented programming,
> which is perhaps why I have not been able to take advantage of existing
> help on this subject. If there are answers, I simply do not understand them!
> 
> *What I need to do:*
> I am trying to generate a table of correlations (26 parameters in columns,
> 60 data rows.) I need Spearman's rho, the significance level p, and I need
> to apply the Bonferroni correction. I also need to export it into Excel in
> a nice matrix-y table format.
> 
> *What I am able to do:*
> I am able to do parts of what I want: with cor() I can generate a Spearman
> correlation (but it's very ugly and hard to read or export nicely).
> With cor.test I can generate a result when I use only 2 parameters (eg.
> soilMois and BD), but I need to produce a table with all parameters
> compared to each other.
> 
> Ex:     result<-cor.test(AprCorKR$SoilMois, AprCorKR$BD,method="spearman",
> adjust.method="Bonferroni")
> 
> Warning message:
> 
> In cor.test.default(AprCorKR$SoilMois, AprCorKR$BD,  :
> 
>   Cannot compute exact p-values with ties
> 
> 
>         Spearman's rank correlation rho
> 
> data:  AprCorKR$SoilMois and AprCorKR$BD
> S = 49274.48, p-value = 0.003705
> alternative hypothesis: true rho is not equal to 0
> sample estimates:
>        rho
> -0.3691158
> 
> (By the way, I understand the warning message.)
> 
> *What I am unable to do:*
> Use the function cor.test to generate the output of all my parameters with
> respect to one another.
> 
> Examples below are of various ways of writing what I want to R:
> > result<-cor.test(AprCorKR$[2:26], AprCorKR$[2:26],method="spearman",
> adjust.method="Bonferroni")
> Error: unexpected '[' in "result<-cor.test(AprCorKR$["
> >
> > result<-cor.test(AprCorKR$[2,26], AprCorKR$[2,26],method="spearman",
> adjust.method="Bonferroni")
> Error: unexpected '[' in "result<-cor.test(AprCorKR$["
> > result<-cor.test(c([2:26]), c([2:26]),data=AprCorKR,method="spearman",
> adjust.method="Bonferroni")
> Error: unexpected '[' in "result<-cor.test(c(["
> > result<-cor.test(vars=2:26, vars=2:26,data=AprCorKR,method="spearman",
> adjust.method="Bonferroni")
> Error in cor.test.default(vars = 2:26, vars = 2:26, data = AprCorKR, method
> = "spearman",  :
>   argument "x" is missing, with no default
> > result<-cor.test(vars=2:26,data=AprCorKR,method="spearman",
> adjust.method="Bonferroni")
> Error in cor.test.default(vars = 2:26, data = AprCorKR, method =
> "spearman",  :
>   argument "x" is missing, with no default
> > result<-cor.test(data=AprCorKR,vars=2:26,method="spearman",
> adjust.method="Bonferroni")
> Error in cor.test.default(data = AprCorKR, vars = 2:26, method =
> "spearman",  :
>   argument "x" is missing, with no default
> 
> *What I know:*
> I have no missing values, no values of zero, and no typos that I am aware
> of. I can display all my data correctly in R (and R seems to read it
> correctly, based on what I am already able to do.)
> 
> A former colleague wrote a small line of code that does what I am trying to
> do but I can't get it to work on my newer version of R. I have R 2.14.2 and
> I don't know what version he was using.
> His code for this: *result<-cor_test(data="**mydata*
> ",vars=2:26,adjust.method="Bonferroni")
> 
> (I include below his entire German instructions. But the code he wrote to
> do this job is very short, and I would like to duplicate it if I can. It
> provides the results I need in a form I can use.)
> 
> * *An additional note:
> I have multtest loaded, and in my version cor.test is not written as
> cor_test with an underscore but rather with a period. In the version of R
> that I am using, there is no such thing as "correlation.r".
> 
> *
> *
> 
> 
> ("Alb_Gesamtdat_10-03-10_bd_NA.txt") und das R-Skript ("correlation.r")
> liegen und das Paket 'multtest' ist installiert
> (www.r-project.org<file:///C:/Users/Regan/Documents/SCALEMIC/NewRStats/www.r-project.org>->
> CRAN
> 
> -> Mirror -> Packages)
> 
> 
> 
> > source("correlation.r")
> 
> 
> 
> > result <- cor_test( data="Alb_Gesamtdat_10-03-10_bd_NA.txt",
> 
> > vars=5:23,
> 
> adjust.method="Bonferroni" )
> 
> 
> 
> Es werden bei diesem Funktionsaufruf automatisch 3 Dateien angelegt:
> 
>   - spearman_correlation_coefficients_complete_data.txt
> 
>   - spearman_correlation_raw_pvalues_complete_data.txt
> 
>   - spearman_correlation_Bonferroni_corrected_pvalues_complete_data.txt
> 
> 
> 
> 
> 
> Thanks to anyone who can help me with this!
> 
> 
> Kind regards,
> 
> Kathy Regan