Man pages for iFad
An integrative factor analysis model for drug-pathway association inference

data_simulationSimulation of example dataset for the factor analysis model
gibbs_samplingGibbs sampling for the inference of the inference of...
iFad-packageAn integrative factor analysis model for drug-pathway...
label_chainUpdated factor label configuration during the Gibbs sampling
matrixL1The matrix representing prior belief for matrixZ1
matrixL2The matrix representing prior belief for matrixZ2
matrixPi1The bernoulli probability matrix for matrixZ1
matrixPi2The bernoulli probability matrix for matrixZ2
matrixPr_chainThe updated posterior probability for matrixZ1&Z2 during...
matrixW1The factor loading matrix representing the gene-pathway...
matrixW2The factor loading matrix representing the drug-pathway...
matrixW_chainThe updated matrixW during the Gibbs sampling
matrixXThe factor activity matrix
matrixX_chainThe updated matrixX in the Gibbs sampling process
matrixY1The gene expression dataset
matrixY2The drug sensitivity matrix
matrixZ1The binary indicator matrix for matrixW1
matrixZ2Binary indictor matrix for matrixW2
matrixZ_chainThe updated matrixZ in the Gibbs sampling process
mcmc_trace_plotTraceplot of the Gibbs sampling iterations
ROC_plotCalculate the AUC (area under curve) and generate ROC plot
sigma1Covariance matrix of the noise term for the genes
sigma2Covariance matrix of the noise term for the drugs
tau_g_chainThe updated tau_g in the Gibbs sampling process
Y1_meanThe mean value used for the simulation of matrixY1
Y2_meanThe mean value used for the simulation of matrixY2
Ymean_compareCompare the infered Y_mean values with the true values
iFad documentation built on May 29, 2017, 8:31 p.m.