rekaya: Estimate misclassification in phenotype using Gibbs Sampler

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 algorithm defined by Rekaya et. al (PMC5138056; 2016) to predict misclassification probabilities using Gibbs Sampling algorithm.

Usage

rekaya(y, x, pi1.prior = c(1, 4), pi2.prior = c(1, 4),
  beta.initial.vec = NULL, iterations = 10000, stamp = 1000,
  verbose = T)

Arguments

y	Phenotype vector with length n.
x	Genotype matrix with dimensions n x i.
pi1.prior	hyperparameters for false positive rate pi1. Default Beta(1, 4).
pi2.prior	hyperparameters for false negative rate pi2. Default Beta(1, 4).
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(mu, pi1, pi2) where mu is mean effect size value, pi1/pi2 false positive/false negative rates.

• 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.