Predict: Predict posterior probabilities for variants to be in each...

In FUNLDA: Genomic Latent Dirichlet Allocation

Description

Predict posterior probabilities for variants to be in each cluster in a fitted LDA model

Usage

Predict(predict.data, summary.c)

Arguments

predict.data

A data frame with character-valued columns rs and cat and numeric-valued columns with annotations. Each row contains data for one SNP in one tissue. rs is an ID for the SNP, which need not be unique, and cat is an ID for each tissue, which must match an ID for which training data was included when fitting the LDA model. Aannotation columns must have column names matching those supplied when fitting the LDA model.

summary.c

A fitted LDA model created using FitLDAModel or FitLDAModelNewTissues.

Value

p.labeled, a data frame with one row per training variant with the posterior probability of each variant to be in each each cluster (with column names CLUSTER1,...) and columns cat and rs.

Examples

## Not run: 
  data(training)
  summary.c <- FitLDAModel(training.data=training, nclust=3,
                         kde.nbins=100, iters=50, inner.iters=50)
  pred <- Predict(predict.data=training, summary.c=summary.c)

## End(Not run)

FUNLDA documentation built on July 11, 2017, 1:01 a.m.