Anders,
Does your data have missing values? It looks like they might. Look at the 'use' parameter in cor. Also, is there a reason to use cor.test instead of cor. Finally, if the expression values are not normal, could you transform them first to make them more so--log2, for example? And, no, no one has figured out the "best" way to define distances/correlations for microarray, at least to the best of my knowledge.
Sean
On Oct 14, 2004, at 8:37 AM, Anders Stegmann wrote:
Hi, R!
Question1:
I am trying to correlate two vectors of numbers (two columns of microarray
signal values) by using the non-parametric Spearman's rank correlation
coefficient rho:
cor.test(V2.Signal,V3.Signal,method="spearman")
but I get the error message:
Error in if (q > (n^3 - n)/6) pspearman(q - 1, n, lower.tail = FALSE) else
pspearman(q, :
missing value where logical needed
In addition: Warning message:
NAs introduced by coercion
I have tried to use the parametric Pearson correlation and the non-parametric Kendall's tau correlation and had no problem with that!!
cor(V2.Signal,V3.Signal,use="complete.obs") (thePearson correlation)
cor.test(V2.Signal,V3.Signal,method="kendall") (theKendall's correlation)
what's wrong?
Question2:
Does anyone accidently know which correlation method would be the most
correct to use when the microarray signal values (the values to be
correlated) are not normal distributed (the Kendall's method seem to fit
better to my other tests than the Pearson method).
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