#Editted for constrained model
oadaLikelihood_SLdom <- function(parVect, nbdadata){
if(is.list(nbdadata)){
totalLikelihood <- 0;
for(i in 1:length(nbdadata)){
subdata <- nbdadata[[i]];
totalLikelihood <- totalLikelihood+ oadaLikelihood_SLdom(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] -1#s parameters
#MInus 1 to account for the fixed reference, s1
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
datalength <- length(nbdadata@id) #ILV effects on social transmission
#Extract vector giving which naive individuals were present in the diffusion for each acqusition event
presentInDiffusion<-nbdadata@ presentInDiffusion
#assign different paramreter values to the right vectors
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(noSParam==0){sParam <- 1}else{sParam <- c(1,parVect[1:noSParam])}
#Extra 1 added to sParam for the reference s1 level
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
asocialLP <- apply(asocialCoef.mat*asocial.sub, MARGIN=1, FUN=sum)
}
asocialLP<-asocialLP+nbdadata@offsetCorrection[,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
socialLP <- apply(intCoef.mat*int.sub, MARGIN=1, FUN=sum)
}
socialLP<-socialLP+nbdadata@offsetCorrection[,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
multiLP <- apply(multiCoef.mat*multi.sub, MARGIN=1, FUN=sum)
}
multiLP<-multiLP+nbdadata@offsetCorrection[,4]
asocialLP<-asocialLP+multiLP
socialLP<-socialLP+multiLP
sParam.mat <- matrix(data=rep(sParam+1, datalength), nrow=datalength, byrow=T) # create a matrix of sParams
unscaled.st <- apply(sParam.mat*nbdadata@stMetric, MARGIN=1, FUN=sum)
unscaled.st<-unscaled.st+nbdadata@offsetCorrection[,1]
#The totalRate is set to zero for naive individuals not in the diffusion for a given event
asocialRate <- exp(asocialLP)* presentInDiffusion
socialRate<- exp(socialLP)*unscaled.st*presentInDiffusion
#Assuming social transmission is dominant, i.e. individuals with non-zero connections always learn before individuals with 0 connections
solverTotalRate<-asocialRate[nbdadata@status==1]*(tapply(socialRate, INDEX=nbdadata@event.id, FUN=sum)==0)+socialRate[nbdadata@status==1]*(tapply(socialRate, INDEX=nbdadata@event.id, FUN=sum)>0)
#Take logs and add across acquisition events
lComp1 <- sum(log(solverTotalRate)) # group by skilled
lComp2.1 <-tapply(asocialRate, INDEX=nbdadata@event.id, FUN=sum)*(tapply(socialRate, INDEX=nbdadata@event.id, FUN=sum)==0)+tapply(socialRate, INDEX=nbdadata@event.id, FUN=sum)
lComp2.2 <- sum(log(lComp2.1))
negloglik <- lComp2.2 - lComp1
return(negloglik)
}
}
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