STIM: Score test with the intensity value

In PedCNV: An implementation for association analysis with CNV data.

Description

Calculates the score test statistics with the intensity value.

Usage

  STIM(envirX, signal, fam, alpha, phi, sig2g, sig2)

Arguments

envirX	The matrix of environmental variables. The intercept should be included if it's needed.
fam	The FAM file which follows the format defined in PLINK.
signal	The matrix of intensity measurements. The row names must be consistent with the Individual ID in fam file.
alpha	The estimated parameters for environmental variables under null hypothesis. This value can be calculated by using function AssoTestProc.
phi	The matrix of correlation between individuals.
sig2g	The estimated standard error for polygenic effect under null hypothesis. This value can be calculated by using function AssoTestProc.
sig2	The estimated standard error for environmental effect under null hypothesis. This value can be calculated by using function AssoTestProc.

Value

It returns the statistic value and pvalue of the score test.

STIMs	The statistic value of score test with the intensity value under null hypothesis.
STIMp	The pvalue of score test with the intensity value under null hypothesis.
df	The degree of freedom of score test with the intenstiy value under null hypothesis.

Author(s)

Meiling Liu, Sungho Won

Examples

# Fit the data under the assumption that there are 3 clusters
asso.fit <- AssoTestProc(signal=signal,fam=fam,envirX=envirX,phi=phi,N=3,varSelection='PC.9')
alpha <- asso.fit$para$alpha
sig2g <- asso.fit$para$sig2g
sig2 <- asso.fit$para$sig2
STIM(envirX=envirX,signal=signal,fam=fam,alpha=alpha,phi=phi,sig2g=sig2g,sig2=sig2)

Example output

Loading required package: Rcpp
Loading required package: RcppArmadillo
Loading required package: ggplot2
The first 5 principal components are used.
The logliklihood for signal model is -1477.954 when clustering number is 3.
Iteration 1:
AI algorithm for REML.
The individuals are correlated, LMM is used.
Iteration 2:
AI algorithm for REML.
The individuals are correlated, LMM is used.
Iteration 3:
AI algorithm for REML.
The individuals are correlated, LMM is used.
$STIMs
         [,1]
[1,] 28.54141

$STIMp
             [,1]
[1,] 0.0001753618

$df
[1] 7

PedCNV documentation built on May 2, 2019, 8:17 a.m.