cea {RedeR} | R Documentation |
Simple function for correlation analysis. This function computes a null distribution via permutation and returns the significant correlation values.
cea(x, sig=0.01, p.adj.method="fdr", cor.method="spearman", nper=1000, plotcea=TRUE, ...)
x |
A matrix or data frame. |
sig |
Significance threshold. |
p.adj.method |
Correction method passed to "p.adjust" function. |
cor.method |
Correlation method passed to "cor" function. |
nper |
Number of permutations. |
plotcea |
Logical value, option to plot density and the null distributions. |
... |
Additional arguments passed to plotcea option. |
Additional arguments:
If plotcea=TRUE, 'n.breaks' sets the number of histogram breaks (Default=100 <integer>).
If plotcea=TRUE, 'plotnull' sets whether to plot the null distribution (Default=TRUE <logical>).
If plotcea=TRUE, 'avnull' takes the average null distribution (Default=TRUE <logical>).
If plotcea=TRUE, 'nullcol' sets the color of the null distribution (Default="black" <character>).
An adjacency matrix with significant correlation values.
Mauro Castro
data(ER.deg) #--- a gene expression matrix exp <- ER.deg$exp #--- a sample from gx!! idx <- sample(1:nrow(exp))[1:100] exp <- exp[idx,] ## Not run: res <- cea(x=exp, nper=100) #ps set 'nper' for at least 1000 ## End(Not run)