correlate {mRMRe} | R Documentation |
Function to compute various correlation measures between two variables
Description
Correlate is a function that cestimates correlation between two variables, which can be either continuous, categorical (ordered factor) or censored (survival data).
Usage
correlate(X, Y, method = c("pearson", "spearman", "kendall", "frequency", "cramersv",
"cindex"),strata, weights, outX = TRUE, bootstrap_count = 0, alpha = 0.05,
alternative = c("two.sided", "less", "greater"))
Arguments
X |
Vector of type numeric, ordered factor, or Surv. |
Y |
Vector of type numeric, ordered factor, or Surv of same length as |
method |
One of the following values: pearson, spearman, kendall, frequency, cramersv, or cindex. |
strata |
Vector of type factor corresponding to the sample strata. |
weights |
Vector of type numeric corresponding to the sample weights. |
outX |
For cindex, if set to |
bootstrap_count |
If set to |
alpha |
The probability of Type I error that is, rejecting a null hypothesis when it is in fact true |
alternative |
a character string specifying the alternative hypothesis,must be one of two.sided (default), greater or less. You can specify just the initial letter. |
Details
The correlate
function could be used to measure correlation between any types of variables:
- numeric vs. numeric
Pearson, Spearman, Kendall or concordance index
- numeric vs. ordered factor
concordance index (Somers' Dxy)
- numeric vs. survival data
concordance index (Somers' Dxy)
- ordered factor vs. ordered factor
Carmer's V
- ordered factor vs. survival data
concordance index (Somers' Dxy)
- survival data vs. survival data
concordance index (Somers' Dxy)
Part of the code underlying correlate
is also used in mim
method of the mRMRe.Data
object because correlations are used to build the mutual information matrix in order for feature selection to take place. This is why these two functions have many argiuments in common.
Value
estimate |
point estimate |
se |
standard error |
lower |
lower confidence bound |
upper |
upper confidence bound |
p |
p-value |
n |
sample size |
Author(s)
Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains
See Also
Examples
set.thread.count(2)
## load data
data(cgps)
## spearman correlation coefficent between the first gene and Camptothecin IC50
correlate(X=cgps.ge[ ,1], Y=cgps.ic50, method="spearman")
## concordance index between the first gene and Camptothecin IC50
correlate(X=cgps.ge[ ,1], Y=cgps.ic50, method="cindex")