BayesKnockdown: Posterior Probabilities for Knockdown Data

In wmchad/BayesKnockdown: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data

Description

Calculates posterior probabilities for edges from a knocked-down gene to each of a set of potential target genes. More generally, it calculates posterior probabilities between a single predictor variable and each of a set of response variables, incorporating prior probabilities potentially unique to each response variable.

Usage

BayesKnockdown(x, y, prior = 0.5, g = sqrt(length(x)))

Arguments

x	n-vector of predictor data. In knockdown experiments, this is a vector of the expression levels of the knocked-down gene across n experiments.
y	Outcome matrix: p (number of outcomes measured) by n (number of samples). In knockdown experiments, this is a matrix of all the gene measurements for genes that were not knocked down.
prior	Prior probabilities for the outcome variables. Defaults to 0.5 for all variables.
g	The value to use for Zellner's g-prior. Defaults to the square root of the number of observations.

Value

A vector of p posterior probabilities indicating the probability of a relationship between the predictor variable and each outcome variable.

Examples

n <- 100;
p <- 10;
x <- rnorm(n);
y <- matrix(nrow=p, data=rnorm(n*p));
y[3,] <- y[3,] + 0.5*x;

BayesKnockdown(x, y);

wmchad/BayesKnockdown documentation built on May 4, 2019, 9:45 a.m.