cor0.test {GeneNet} | R Documentation |
Test of Vanishing (Partial) Correlation
Description
cor0.test
computes a p-value for the two-sided test with the null
hypothesis H0: rho == 0 versus the alternative hypothesis HA: rho != 0.
If method="student"
is selected then the statistic
t=r*sqrt((kappa-1)/(1-r*r))
is considered which under H0 is
student-t distributed with df=kappa-1
. This method is exact.
If method="dcor0"
is selected then the p-value is computed
directly from the null distribution of the (partial) correlation
(see dcor0
).
This method is also exact.
If method="ztransform"
is selected then the p-value is computed
using the z-transform (see z.transform
), i.e. using
a suitable chosen normal distribution.
This method returns approximate p-values.
Usage
cor0.test(r, kappa, method=c("student", "dcor0", "ztransform"))
Arguments
r |
observed correlation |
kappa |
degree of freedom of the null-distribution |
method |
method used to compute the p-value |
Value
A p-value.
Author(s)
Juliane Sch\"afer and Korbinian Strimmer (https://strimmerlab.github.io).
See Also
Examples
# load GeneNet library
library("GeneNet")
# covariance matrix
m.cov <- rbind(
c(3,1,1,0),
c(1,3,0,1),
c(1,0,2,0),
c(0,1,0,2)
)
# compute partial correlations
m.pcor <- cor2pcor(m.cov)
m.pcor
# corresponding p-values
# assuming a sample size of 25, i.e. kappa=22
kappa2n(22, 4)
cor0.test(m.pcor, kappa=22)
cor0.test(m.pcor, kappa=22) < 0.05
# p-values become smaller with larger r
cor0.test(0.7, 12)
cor0.test(0.8, 12)
cor0.test(0.9, 12)
# comparison of various methods
cor0.test(0.2, 45, method="student")
cor0.test(0.2, 45, method="dcor0")
cor0.test(0.2, 45, method="ztransform")