minPcom: Compute the p-value for ComCop test
In diptavo/MultiSKAT: Sequence Kernel Association Tests with Multiple phenotypes

Description Usage Arguments Details Value Author(s) Examples

This function combines the choices for Σ G and Σ p kernel in a single grand omnibus test. The p-values are obtained by the minimum p-value statistic using copula.

minPcom(obj.res,Sigma_Ps,Z,resample = 200,
weights.beta=c(1,25),kernel = "linear.weighted", Is.Common=FALSE, 
weights = NULL, impute.method = "fixed",
is_check_genotype=TRUE, is_dosage = FALSE, 
missing_cutoff=0.15, estimate_MAF=1,max_maf=1,verbose = TRUE)

`obj.res`	a NULL object output from MultiSKAT_NULL or MultiSKAT_NULL_Kins
`Sigma_Ps`	A list of Σ p kernels to be combined
`Z`	Genotype matrix
`resample`	Number of resampling iterations to obtain the null p-values(default = 200)
`kernel`	a type of kernel (default= "linear.weighted"). See detail section.
`Is.Common`	a binary variable indiciating whether a variant has the same effect on all the phenotypes (default=FALSE).
`weights.beta`	a numeric vector of parameters for the beta weights for the weighted kernels. If you want to use your own weights, please use the "weights" parameter. It will be ignored if "weights" parameter is not null.
`weights`	a numeric vector of weights for the weighted kernels.
`impute.method`	a method to impute missing genotypes (default= "fixed"). "bestguess" imputes missing genotypes as most likely values (0,1,2), "random" imputes missing genotypes by generating binomial(2,p) random variables (p is the MAF), and "fixed" imputes missing genotypes by assigning the mean genotype values (2p).
`is_check_genotype`	a logical value indicating whether to check the validity of the genotype matrix Z (default= TRUE). If Z has non-SNP data, please set it FALSE, otherwise you will get an error message. If it is FALSE and you use weighted kernels, the weights should be given through the "weights" parameter.
`is_dosage`	a logical value indicating whether the matrix Z is a dosage matrix. If it is TRUE, the function will ignore "is_check_genotype".
`missing_cutoff`	a cutoff of the missing rates of SNPs (default=0.15). Any SNPs with missing rates higher than the cutoff will be excluded from the analysis.
`estimate_MAF`	a numeric value indicating how to estimate MAFs for the weight calculation and the missing genotype imputation. If estimate_MAF=1 (default), it uses all samples to estimate MAFs. If estimate_MAF=2, only samples with non-missing phenotypes and covariates are used to estimate MAFs
`max_maf`	a cutoff of the maximum minor allele frequencies (MAF) (default=1, no cutoff). Any SNPs with MAF > cutoff will be excluded from the analysis
`verbose`	a binary indicator to display messages (default=TRUE, displays messages)

This is a copula based test of association for minimum p-value by combining the MultiSKAT tests with phenotype kernel corresponding to Sigma_Ps and SKAT and burden as genotype kernels. It is not advisable to have more than 4 kernels in the Sigma_Ps list.

`p.value`	p-value of the minPcom test
`obj.SKAT`	list of output objects from MultiSKAT test with the kernels in Sigma_Ps and SKAT as genotype kernel (r.corr = 0).
`obj.Burden`	list of output objects from MultiSKAT test with the kernels in Sigma_Ps and burden as genotype kernel (r.corr = 1).

Diptavo Dutta

data(MultiSKAT.example.data)
attach(MultiSKAT.example.data)

obj.null <- MultiSKAT_NULL(Phenotypes,Cov)
out1 <- MultiSKAT(obj.null,Genotypes,cov(Phenotypes))
out2 <- MultiSKAT(obj.null,Genotypes,diag(5))
out3 <- MultiSKAT(obj.null,Genotypes,matrix(1,ncol = 5,nrow = 5))

### Getting minPcom p-value combining PhC, Het and Hom kernels
Sigma_Ps = list(cov(Phenotypes),diag(5),matrix(1,ncol = 5,nrow = 5))
obj.com = minPcom(obj.null,Sigma_Ps,Genotypes)
str(obj.com)