qtcatHit: Fitting Hierarchical Inference Testing

In QTCAT/qtcat: Quantitative Trait Cluster Association Test

Description

Hierarchical inference testing for phenotype-SNP association.

Usage

qtcatHit(pheno, geno, B = 50, p.samp1 = 0.35, nfolds = 5,
  overall.lambda = FALSE, lambda.opt = "lambda.1se", alpha = 1,
  gamma = seq(0.05, 0.99, by = 0.01), max.p.esti = 1, seed = 12321,
  mc.cores = 1, trace = FALSE, ...)

Arguments

pheno	an object of class qtcatPheno.
geno	an object of class qtcatGeno.
B	a integer indicating the number of sample-splits.
p.samp1	a value specifying the fraction of data used for the LASSO sample-split. The ANOVA sample-split is 1 - p.samp1.
nfolds	Number of folds (default is 5). See cv.glmnet for more details.
overall.lambda	Logical, if true, lambda is estimated once, if false (default), lambda is estimated for each sample split.
lambda.opt	a criterion for optimum selection of cross validated lasso. Either "lambda.1se" (default) or "lambda.min". See cv.glmnet for more details.
alpha	a single value in the range of 0 to 1 for the elastic net mixing parameter.
gamma	a vector of gamma-values used in significance estimation.
max.p.esti	a maximum for computed p-values. All p-values above this value are set to one. Small max.p.esti values reduce computing time.
seed	a RNG seed, see set.seed.
mc.cores	a number of cores for parallelising. The maximum is 'B'. For details see mclapply.
trace	logical, if TRUE it prints the current status of the program.
...	additional arguments for cv.glmnet.

Examples

# If you want to run the examples, use:
# example(qtcatHit, run.dontrun = TRUE)
## Not run: 
# files containing example data for SNP data and the phenotype
pfile <- system.file("extdata/phenodata.csv", package = "qtcat")
gfile <- system.file("extdata/snpdata.csv", package = "qtcat")
pdat <- read.csv(pfile, header = TRUE)
snp <- read.snpData(gfile, sep = ",")
clust <- qtcatClust(snp)
geno <- qtcatGeno(snp, clust)
pheno <- qtcatPheno(names = pdat[, 1],
                    pheno = pdat[, 2],
                    covariates = model.matrix(~ pdat[, 3]))

# fitting HIT
fitted <- qtcatHit(pheno, geno)

## End(Not run)

QTCAT/qtcat documentation built on April 20, 2021, 11:20 p.m.