BayesKnockdown.es: Posterior Probabilities for ExpressionSet Data

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

Description

Calculates posterior probabilities for an ExpressionSet object by defining one feature as the predictor. Each other feature in the ExpressionSet is is then used as a response variable and posterior probabilities are calculated, incorporating prior probabilities potentially unique to each response variable.

Usage

BayesKnockdown.es(es, predFeature, prior = 0.5, g = sqrt(dims(es)[2,1]))

Arguments

es	An ExpressionSet object with p features and n samples.
predFeature	The name of the feature to use as the predictor.
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-1 posterior probabilities indicating the probability of a relationship between the predictor variable and each outcome variable.

Examples

library(Biobase);
data(sample.ExpressionSet);
subset <- sample.ExpressionSet[1:10,];

BayesKnockdown.es(subset, "AFFX-MurIL10_at");

BayesKnockdown documentation built on Nov. 8, 2020, 5:48 p.m.