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

Description Usage Arguments Value Author(s) Examples

View source: R/AssoTestProcCS_mix.R

This function tests the association of CNV with continuous trait of interest. Two statistics are provided for different strategies with the intensity measurement.

  AssoTestProc(signal, fam, envirX, phi, N,
    varSelection = c("PC1", "RAW", "PC.9", "MEAN"),
    H0 = TRUE, threshold = 1e-05, itermax = 8,
    thresEM = 0.005, thresAI = 1e-05)

`signal`	The matrix of intensity measurements. The row names must be consistent with the Individual ID in fam file.
`fam`	The FAM file which follows the format defined in PLINK.
`envirX`	The matrix of environmental variables. The intercept is automatically included and it does not need to be in this matrix.
`phi`	The correlation matrix between individuals. It can be built with the kinship coefficient or the estimated correlation matrix with SNP data. Free software that builds this matrix is available, and one of them can be downloaded at http://biostat.ac.kr/fqls/ The default is an identity matrix and it is for independent samples.
`N`	Number of clusters one wants to fit to the data. N needs to be larger than 1 and if it is 1, error will be returned. It can be estimated with the function `ClusProc`.
`varSelection`	Factor. For specifying how to handle the intensity values. It must take value on 'RAW', 'PC.9', 'PC1'and 'MEAN'. If the value is 'RAW', then the raw intensity value will be used. If it is 'PC.9', then the first several PCA scores which account for 90% of all the variance will be used. If the value is 'PC1', then the first PCA scores will be used. If the value is 'MEAN', the mean of all the probes will be used. The default method is 'PC1'.
`H0`	Logicals. If it is TRUE (the default), all parameters are estimated under the assumption that there is no genetic association between CNV and phenotypes. If it is FALSE, parameters are estimated under the null or alternative hypothesis.
`threshold`	Optional number of convergence threshold. The iteration stops if the absolute difference of log likelihood between successive iterations is less than it. The default threshold 1e-05 will be used if it's missing.
`itermax`	Optional. The iteration stops if the times of iteration is large than this value. The default number 8 will be used if it's missing.
`thresEM`	Optional number of convergence threshold in the EM (expectation-maximization method) procedure. The default threshold 0.005 will be used if it's missing.
`thresAI`	Optional number of convergence threshold in the AI (average information method) procedure. The default threshold 1e-05 will be used if it's missing.

It returns object of class 'asso'. The result is obtained under the null hypothesis if H0 is TRUE, otherwise the result is obtained under null or alternative hypothesis.

`para`	The parameter estimations for the best fit.
`clusRes`	The clustering assignment for each individual.

Meiling Liu, Sungho Won and Weicheng Zhu

1 2	# Fit the data under the assumption that there are 3 clusters fit.pc <- AssoTestProc(signal=signal,fam=fam,envirX=envirX,phi=phi,N=3,varSelection='PC.9')