cor0.estimate.kappa {GeneTS}  R Documentation 
cor0.estimate.kappa
estimates the degree of freedom kappa
in the
nulldistribution of the correlation coefficient (i.e. assuming that rho=0).
According to Fisher's rule kappa = round(1/var(z) + 2)
the degree of freedom
can be estimated from the variance of the ztransformed sample correlations.
Maximumlikelihood estimates of the degree of freedom is obtained
on the basis of the null distribution of the sample correlation coefficient
(i.e. assuming rho = 0) using method="likelihood"
. This results
almost always in the same estimate of kappa as with the simple Fisher's rule.
If method="robust"
then the variance employed in Fisher's rule
is estimated using the Huber Mestimate of the scale. This is useful
if the nulldistribution is slightly "contaminated".
The degree of freedom kappa
depends both on the sample size N and the number
G of investigated variables,
i.e. whether simple or partial correlation coefficients are being considered.
For G=2 (simple correlation coefficient) the degree of freedom equals kappa = N1,
whereas for arbitrary G (with G2 variables eliminated in the partial correlation coefficient)
kappa = NG+1 (see also dcor0
and kappa2N
).
If the empirical sampling distribution is a mixture
distribution then use of cor0.estimate.kappa
may not be appropriate;
instead cor.fit.mixture
may be used.
cor0.estimate.kappa(r, method=c("fisher", "likelihood", "robust"), MAXKAPPA=5000, w=1.0)
r 
vector of sample correlations (assumed true value of rho=0) 
method 
use Fisher's rule (fisher ),
optimize likelihood function of null distribution (likelihood ), or
use Fisher's rule with robust estimate of variance (robust ),

MAXKAPPA 
upper bound for the estimated kappa (default: MAXKAPPA=5000); only for likelihood estimate 
w 
winsorize at `w' standard deviations; only for robust estimate 
The estimated degree of freedom kappa.
Juliane Schaefer (http://www.stat.unimuenchen.de/~schaefer/) and Korbinian Strimmer (http://www.stat.unimuenchen.de/~strimmer/).
dcor0
, z.transform
,
hubers
, kappa2N
, cor.fit.mixture
.
# load GeneTS library library(GeneTS) # distribution of r for kappa=7 x < seq(1,1,0.01) y < dcor0(x, kappa=7) # simulated data r < rcor0(1000, kappa=7) hist(r, freq=FALSE, xlim=c(1,1), ylim=c(0,5)) lines(x,y,type="l") # estimate kappa cor0.estimate.kappa(r)