phelex_mh: Estimate misclassification in phenotype

In afrahshafquat/phelex: Estimate misclassification probabilities for GWAS Phenotypes

Description

Estimates misclassification probabilities in observed GWAS phenotype y given genotypes dataset x The method follows the PheLEx-mh algorithm to predict misclassification probabilities using Gibbs sampling and mixed model defined by Shafquat et al.

Usage

phelex_mh(y, x, A, pi1.prior = c(1, 4), pi2.prior = c(1, 4),
  sigmaA.initial = 0.01, u.initial = NULL, beta.initial.vec = NULL,
  iterations = 10000, stamp = 1000, verbose = T)

Arguments

y	Phenotype vector with length n.
x	Genotype matrix with dimensions n x m.
A	Genetic relatedness matrix with dimensions n x n.
pi1.prior	hyperparameters for false positive rate pi1.
pi2.prior	hyperparameters for false negative rate pi2.
sigmaA.initial	Starting value for variance parameter sigmaA where u ~ MVN(0, sigmaA*A).
u.initial	Starting values for random effects vector u. Default assumes random values.
beta.initial.vec	Initial values for beta parameters in order c(Beta_a_1,...Beta_a_i). Default values are random values for all parameters.
iterations	Number of iterations for sampling
stamp	Iteration breakpoint to print time
verbose	Default TRUE. Prints progress information

Value

List containing

• betas: Matrix of estimated effect sizes for each SNP (SNPs[rows] x iterations[columns]).

• parameters: Matrix with estimated parameter values[rows] across iterations[columns]. Order is c(sigmaA, pi1, pi2) where pi1/pi2 = false positive/false negative rates, sigmaA = variance parameter

• misclassified.cases: Matrix of misclassification indicators where 1s represent false positives and 0s represent true positives as inferred at each iterations

• misclassified.controls: Matrix of misclassification indicators where 1s represent false negatives and 0s represent true negatives as inferred at each iterations

afrahshafquat/phelex documentation built on Feb. 5, 2020, 7:44 p.m.