R/ISOpure.model_optimize.vv.vv_loglikelihood.R

In ISOpureR: Deconvolution of Tumour Profiles

# The ISOpureR package is copyright (c) 2014 Ontario Institute for Cancer Research (OICR)
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### FUNCTION: ISOpure.model_optimize.vv.vv_loglikelihood.R ##################################################################
#
# Input variables:
#   ww: log(vv-1), a Kx1 matrix
#   sum_log_theta: the column sums of log(theta), a 1xK matrix
#   DD: the number of patients (a scalar)
# 
# Output variables:
#   loglikelihood: loglikelihood function relevant to vv

ISOpure.model_optimize.vv.vv_loglikelihood <- function(ww, sum_log_theta, DD) {

	# K = number of normal profiles + 1 
	K <- length(ww); 
	ww <- matrix(ww, nrow=1, ncol=K);
	sum_log_theta <- matrix(sum_log_theta, nrow=1, ncol=K);
	# a is actually the current vv vector
	a <- exp(ww) + 1; 
	
	# the loglikelihood calculation
	# log p(theta_n|vv) = log Dirichlet(theta_n|vv)
	loglikelihood <- DD * (lgamma(sum(a)) - sum(lgamma(a))) +  (a-1)%*%t(sum_log_theta);

	# take negative because we are using a minimizer
	loglikelihood <- -loglikelihood;
	return(loglikelihood);
}