calculates a parameter-non-optimized log-likelihood using a dirichlet multinomial model given a matrix of data (k), a model matrix with the features (mm), and a concentration parameter. This is useful for data sets that fail to converge the optimization process. e.g. those with not so many samples.

calculates a parameter-non-optimized log-likelihood using a dirichlet multinomial model given a matrix of data (k), a model matrix with the features (mm), and a concentration parameter. This is useful for data sets that fail to converge the optimization process. e.g. those with not so many samples.

Usage

rough_dirmnom_ll(k, mm, conc)

Arguments

k

A matrix with the data to fit a dirichlet multinomial model.

mm

A model matrix with the features.

conc

The concentration parameter that accounts for overdispersion. To estimate this value use the estimate_overdispersion() function.

Value

The log-likelihood for the fit.