phelex_mm: 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-m algorithm to predict misclassification probabilities using Adaptive Metropolis-Hastings defined by Shafquat et al.

Usage

phelex_mm(x, y, iterations = 1e+05, alpha.prior = c(10, 1),
  lambda.prior = c(1, 1), link = "pnorm", beta.prior = "norm",
  beta.prior.params = c(0, 1), beta.initial.vec = NULL, mu.update = 0.5,
  verbose = TRUE, normalize = FALSE, stamp = 1000)

Arguments

x	Genotype matrix with dimensions n x m.
y	Phenotype vector with length n.
iterations	Total number of iterations for Metropolis-Hastings Sampling
alpha.prior	Beta prior parameters for true-positve rate
lambda.prior	Beta prior parameters for false-positive rate.
link	probit or logistic model (options pnorm and plogis respectively)
beta.prior	'norm'(default): Normal prior or 'unif' Uniform prior on fixed effects
beta.prior.params	if beta.prior is norm, then (mean, sd), if unif then (min, max)
beta.initial.vec	Vector of initial values for beta parameters i.e. effect sizes for n snps.
mu.update	Fractions of iterations to start updating mean fixed effects or mu. Default = 0.5
verbose	Default TRUE. Prints progress information
normalize	Default FALSE. Scales liability computed
stamp	Iteration breakpoint to print time

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, alpha, lambda) where alpha = true positive rate, lambda = false positive rate, mu = mean effect size value

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

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

• posterior: Vector of posterior probability across iterations

• accept: Vector of 1/0 values across iterations; 1 indicates proposal was accepted at iteration;0 o/w

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