apply.rGLM.test {rGLM}R Documentation

Return a permutation p-value of the haplotype effect test based on rGLM approach

Description

This function returns a permutation p-value on the likelihood ratio like test of haplotype effect comparing two nested models fit with rGLM function, and an AIC criteria is used to choose a tuning parameter.

Usage

apply.rGLM.test(nonSNPcolumns,hypoDat, family=binomial, allelic=TRUE, pooling.tol = 0.05,maxit=200,tol=0.001,lambda,Bpermu=100,trace=FALSE,hightrace=FALSE)

Arguments

nonSNPcolumns number of columns that do not contain genotype information in hypoDat data set
hypoDat data set that contains the response variable and genotypes on SNPs
family binomial, poisson, gaussian or freq are supported, default=binomial
allelic TRUE if single-locus SNP genotypes are in allelic format and FALSE if in genotypic format; default is TRUE.
pooling.tol pooling tolerance- by default set to 0.05
maxit maximum number of iterations of the EM algorithm; default=50
tol convergence tolerance in terms of either the maximum difference in parameter estimates between iterations or the maximum relative difference in parameter estimates between iterations, which ever is larger. Default is 0.001.
lambda tuning parameters lambda (positive), if it contains more than one positive number, an AIC criteria will be applied to choose a lambda; default is 0.18.
Bpermu number of permutations when calculating the simulated p-values; default=100.
trace indicates whether or not some internal results should be printed; default is FALSE.
hightrace indicates whether or not some internal results should be printed in one.rGLM.test function; default is FALSE.

Details

obs.rGLM is obtained from the one.rGLM.test function based on the observed data set, which is a list and the details cound be found in one.rGLM.test function.

Value

obs.rGLM rGLM results obtained form the one.rGLM.test function on the observed data set
obs.aic.lambda choosen lambda by AIC criteria in the observed data set
obs.beta.trace estimated betas on each tuning parameter lambda based on the observed data set
lambda same as the input lambda
permu.aic.lambda choosed lambdas by AIC criteria in all permutations
permu.LRTstat LRT test statistics on each permutaion
permu.pvalue proportion that observed LRT statistic is no more than permuted LRT statistic
number.of.permu same as the input Bpermu

References

Guo, W. and Lin, S. 2009. Generalized linear modeling with regularization for detecting common disease rare haplotype association. Genetics Epidemiology. DOI: 10.1002/gepi.20382.


[Package rGLM version 2.0 Index]