#Now corrected for trueTies
asocialLikelihood <- function(parVect, nbdadata, retainInt=NULL){
#We need to know whether to remove the interaction variables. This depends on whether an offset is included for any of the s parameters in any of the diffusions.
#This will be passed on by the model fitting function, but if the function is called independently we need to calculate this here
if(is.null(retainInt)){
if(is.list(nbdadata)){
retainInt<-FALSE
for (i in 1:length(nbdadata)){
nbdadataTemp2<-nbdadata[[i]];
if(sum(nbdadataTemp2@offsetCorrection[,1])>0) retainInt<-TRUE
}
}else{
retainInt<-sum(nbdadata@offsetCorrection[,1])>0
}
}
if(is.list(nbdadata)){
totalLikelihood <- 0;
for(i in 1:length(nbdadata)){
subdata <- nbdadata[[i]];
totalLikelihood <- totalLikelihood + asocialLikelihood(parVect= parVect, nbdadata=subdata,retainInt = retainInt);
}
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
noILVmulti<- dim(nbdadata@multiILVdata)[2] #ILV multiplicative model effects
noILVint<- dim(nbdadata@intILVdata)[2] #ILV effects on social learning
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]
#Extend par to include 0 s parameters
parVect<-c(rep(0,noSParam),parVect)
#Allow for the fact that the user might provide offsets to the s parameters which might need to be accounted for
if(retainInt){
noILVint<- dim(nbdadata@intILVdata)[2] #ILV effects on interation (social learning)
if(nbdadata@int_ilv[1]=="ILVabsent") noILVint<-0
if(length(parVect)!=noSParam+noILVasoc+noILVint+noILVmulti){
cat("Error: parVect wrong length. \nNote a non-zero offset is provided for the s parameters. \nparVect must include values for the interaction effects\n")
return(NA)
}
}else{
noILVint<-0
if(length(parVect)!=noSParam+noILVasoc+noILVint+noILVmulti){
cat("Error: parVect wrong length. \nNote a zero offset is provided for the s parameters. \nparVect must not include values for the interaction effects\n")
return(NA)
}
}
#assign different paramreter values to the right vectors
sParam <- parVect[1:noSParam]
asocialCoef <- parVect[(noSParam+1):(noSParam+ noILVasoc)]
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 calculate the multiplicative LP and add to the asocial LP
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
unscaled.st<-nbdadata@offsetCorrection[includeInOADA,1]
#Allow for the fact that the user might provide offsets to the s parameters which might need to be accounted for
if(retainInt){
#assign different paramreter values to the right vectors
intCoef<- parVect[(noSParam+noILVasoc+1):(noSParam+ noILVasoc+noILVint)]
#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)
}
# calculate
socialLP<-socialLP+nbdadata@offsetCorrection[includeInOADA,3]+multiLP
}else{socialLP<-rep(0,datalength)}
#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+asocialCorrectTrueTies(parVect[-(1:noSParam)],nbdadata)
}
return(negloglik)
}
}
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