R/oadalikelihood.R

In whoppitt/NBDA: A Package For Implementing Network-Based Diffusion Analysis

#Now corrected for trueTies

oadaLikelihood <- function(parVect, nbdadata){

  if(is.list(nbdadata)){

    totalLikelihood <- 0;

    for(i in 1:length(nbdadata)){
      subdata <- nbdadata[[i]];
      totalLikelihood <- totalLikelihood+ oadaLikelihood(parVect= parVect, nbdadata=subdata);
    }

    return(totalLikelihood);

  }else{

	#Define required function
	sumWithoutNA <- function(x) sum(na.omit(x))

	#calculate the number of each type of parameter
	noSParam <- dim(nbdadata@stMetric)[2] #s parameters
	noILVasoc<- dim(nbdadata@asocILVdata)[2] #ILV effects on asocial learning
	noILVint<- dim(nbdadata@intILVdata)[2] #ILV effects on interation (social learning)
	noILVmulti<- dim(nbdadata@multiILVdata)[2] #ILV multiplicative model effects

	if(nbdadata@asoc_ilv[1]=="ILVabsent") noILVasoc<-0
	if(nbdadata@int_ilv[1]=="ILVabsent") noILVint<-0
  if(nbdadata@multi_ilv[1]=="ILVabsent") noILVmulti<-0

  includeInOADA<-nbdadata@event.id %in% nbdadata@event.id[nbdadata@status==1]
  #Exclude the lines of data corresponding to the final period to endtime, if necedssary
  datalength <- sum(includeInOADA)

	#Extract vector giving which naive individuals were present in the diffusion for each acqusition event
	presentInDiffusion<-nbdadata@ presentInDiffusion[includeInOADA]

	#assign different paramreter values to the right vectors
	sParam <- parVect[1:noSParam]
	asocialCoef <- parVect[(noSParam+1):(noSParam+ noILVasoc)]
	intCoef<- parVect[(noSParam+noILVasoc+1):(noSParam+ noILVasoc+noILVint)]
	multiCoef<-parVect[(noSParam+noILVasoc+noILVint+1):(noSParam+ noILVasoc+noILVint+noILVmulti)]

	if(nbdadata@asoc_ilv[1]=="ILVabsent") asocialCoef<-NULL
	if(nbdadata@int_ilv[1]=="ILVabsent") intCoef<-NULL
	if(nbdadata@multi_ilv[1]=="ILVabsent") multiCoef<-NULL

	# create a matrix of the coefficients to multiply by the observed data values, only if there are asocial variables
	if(nbdadata@asoc_ilv[1]=="ILVabsent"){
	  asocialLP<-rep(0,datalength)
	}else{
  	asocialCoef.mat <- matrix(data=rep(asocialCoef, datalength), nrow=datalength, byrow=T)
	  asocial.sub <- nbdadata@asocILVdata[includeInOADA,]
	  asocialLP <- apply(asocialCoef.mat*asocial.sub, MARGIN=1, FUN=sum)
	}
	asocialLP<-asocialLP+nbdadata@offsetCorrection[includeInOADA,2]

	# now do the same for the interaction variables
	if(nbdadata@int_ilv[1]=="ILVabsent"){
	  socialLP<-rep(0,datalength)
	}else{
	  intCoef.mat <- matrix(data=rep(intCoef, datalength), nrow=datalength, byrow=T)
	  int.sub <- nbdadata@intILVdata[includeInOADA,]
	  socialLP <- apply(intCoef.mat*int.sub, MARGIN=1, FUN=sum)
	}
	socialLP<-socialLP+nbdadata@offsetCorrection[includeInOADA,3]

	# now adjust both LPs for the variables specified to have a multiplicative effect (the same effect on asocial and social learning)
	if(nbdadata@multi_ilv[1]=="ILVabsent"){
	  multiLP<-rep(0,datalength)
	}else{
	  multiCoef.mat <- matrix(data=rep(multiCoef, datalength), nrow=datalength, byrow=T)
	  multi.sub <- nbdadata@multiILVdata[includeInOADA,]
	  multiLP <- apply(multiCoef.mat*multi.sub, MARGIN=1, FUN=sum)
	}
	multiLP<-multiLP+nbdadata@offsetCorrection[includeInOADA,4]
	asocialLP<-asocialLP+multiLP
	socialLP<-socialLP+multiLP

	sParam.mat <- matrix(data=rep(sParam, datalength), nrow=datalength, byrow=T) # create a matrix of sParams
	unscaled.st <- apply(sParam.mat*nbdadata@stMetric[includeInOADA,], MARGIN=1, FUN=sum)
	unscaled.st<-unscaled.st+nbdadata@offsetCorrection[includeInOADA,1]

	#The totalRate is set to zero for naive individuals not in the diffusion for a given event
	totalRate <- (exp(asocialLP) + exp(socialLP)*unscaled.st)* presentInDiffusion

	#Take logs and add across acquisition events
	lComp1 <- sum(log(totalRate[nbdadata@status==1])) # group by skilled

	lComp2.1 <- tapply(totalRate, INDEX=nbdadata@event.id[includeInOADA], FUN=sum) # check this works. this is total rate per event across all naive id
	lComp2.2 <- sum(log(lComp2.1))

	negloglik <- lComp2.2 - lComp1

	if(!is.null(nbdadata@trueTies[[1]])){
	  negloglik<-negloglik+correctTrueTies(parVect,nbdadata)
	}

	return(negloglik)
	}
}