R/ISOpureS1.model_optimize.omega.omega_compute_loglikelihood.R

Defines functions ISOpureS1.model_optimize.omega.omega_compute_loglikelihood

Documented in ISOpureS1.model_optimize.omega.omega_compute_loglikelihood

# The ISOpureR package is copyright (c) 2014 Ontario Institute for Cancer Research (OICR)
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### FUNCTION: ISOpureS1.model_optimize.omega.omega_compute_loglikelihood.R #######################################################
#
# Input variables:
#   omega: (K-1)x1 matrix representing the weights of the normal profiles B_i used to
#          make the weighted combination that forms the mean parameter vector for the 
#          Dirichlet distribution over m
#   tumordata: a GxD matrix representing gene expression profiles of tumor samples
#   model: list containing all the parameters to be optimized
# 
# Output variables:
#    loglikelihood: computes the part of the loglikelihood function relevant to optimizing omega 

ISOpureS1.model_optimize.omega.omega_compute_loglikelihood <- function(omega, tumordata, model) {
	
	omega <- matrix(omega, nrow=1, ncol=length(omega));
	
	kappa <- model$kappa;
	kappaomegaPP <- as.numeric(model$kappa) %*% omega %*% model$PPtranspose;
	
	omegaPP <- omega %*% model$PPtranspose;
	kappaPP <- as.numeric(kappa)  *  model$PPtranspose;

	# log p(m|k,B,omega) = log Dirichlet(m | k*B*omega )
	loglikelihood <- lgamma(sum(kappaomegaPP)) - sum(lgamma(kappaomegaPP)) + ((kappaomegaPP-1) %*% (t(model$log_all_rates[nrow(model$log_all_rates), ,drop=FALSE])));
	
	return(loglikelihood)
}

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ISOpureR documentation built on May 2, 2018, 1:04 a.m.