DirichletMultinomial (version 1.14.0)

model components: Access model components.

Description

The accessors mixture and mixturewt return information about the esimtated Dirichlet comonents of the fitted model. mixture returns a sample x component matrix of estimated values, mixturewt reta matrix of

Usage

mixture(object, ..., assign=FALSE)
mixturewt(object, ...)
goodnessOfFit(object, ...)
laplace(object, ...)
"AIC"(object, ..., k = 2)
"BIC"(object, ...)
"fitted"(object, ..., scale=FALSE)
"predict"(object, newdata, ..., logevidence=FALSE)
"fitted"(object, ...)
"predict"(object, newdata, ..., assign=FALSE)
"summary"(object, ...)

Arguments

object

An instance of class dmn.

newdata

A matrix of new sample x taxon data to be fitted to the model of object.

...

Additional arguments, available to methods, when applicable.

assign

logical(1) indicating whether the maximum per-sample mixture component should be returned (assign=FALSE), or the full mixture matrix (assign=TRUE).

scale

logical(1) indicating whether fitted values should be returned unscaled (default, scaled=FALSE) or scaled by the variability of mixturewt parameter theta.

logevidence

logical(1) indicating whether posterior probability (default, logevidence=FALSE) or log evidence logical=TRUE should be returned.

k

ignored.

Value

mixture with assign=FALSE returns a matrix of sample x Dirichlet component estimates. With assign=TRUE mixture returns a named vector indexing the maximal Dirichlet component of each sample.mixturewt returns a matrix with rows corresponding to mixture components, and columns pi (component weight) and theta (component variability). Small values of theta correspond to highly variable components.goodnessOfFit returns a named numeric vector of measures of goodness of fit.laplace, AIC, and BIC return the corresponding measures of goodness of fit.

Examples

data(fit)
best <- fit[[4]]
mixturewt(best)
head(mixture(best), 3)
head(mixture(best, assign=TRUE), 3)
goodnessOfFit(best)

fl <- system.file(package="DirichletMultinomial", "extdata",
                  "Twins.csv")
count <- t(as.matrix(read.csv(fl, row.names=1)))
data(bestgrp)
bestgrp
head(predict(bestgrp, count))