Increasing Parameters Decreases Likelihood?

Hiya folks,
I ran a set of CJS models (more or less from Yackulic et al. 2020) including a CJS with a time-independent phi and one with a time-dependent phi. I stored the point wise log-likelihoods for the sake of model comparison using loo, but I also wanted to calculate the overall log likelihoods (ultimately to calculate a % variance explained by some annual climate variable, similar to the ANODEV in Program MARK). I summed up the point wise log-likelihoods from the time-independent phi model to get an overall log-likelihood per MCMC iteration, and repeated this for the time-dependent phi model, and discovered that the likelihood for the time-independent model was often higher than that for the time-dependent model for many of the species I had fit models to. This seems odd to me as all else equal, I would think that having a phi per year as opposed to a single phi should increase the likelihood. Could anyone explain how this could happen… or where in my code I’ve messed up to produce this result?

Here is the time-independent model [phi(.)p(.)]:

``````
data{
int<lower = 1> NsumCH; // number of capture histories
int<lower = 1> n_occasions; // number of capture occasions
int<lower = 1, upper = 2> sumCH[NsumCH, n_occasions]; // capture histories (1,2)
int<lower = 1> sumf[NsumCH]; // first capture occasion
int<lower = 1> sumFR[NsumCH]; // frequency of each capture history
int<lower = 1> n_missing; // number of unsampled years
int<lower = 1> missing[n_missing]; // unsampled years
int<lower = 1> n_observed; // number of sampled years
int<lower = 1> observed[n_observed]; // sampled years
vector[n_occasions-1] temp; // temperature each year
vector[n_occasions-1] prec; // precipitation each year
int n_lik; // number of data to calculate a likelihood for
int<lower = 0, upper = 1> CH[NsumCH, n_occasions]; // capture histories (1,0)
}

parameters{
real<lower=0,upper=1> phi; // survival probability
real<lower = 0, upper = 1> real_p; // detection probability for all years
}

transformed parameters{
simplex[2] tr[2,n_occasions - 1]; // survival likelihoods for marginalization
simplex[2] pmat[2,n_occasions - 1]; // detection likelihoods for marginalization
// real mu[n_occasions-1];
real<lower = 0, upper = 1> p[n_occasions - 1]; // detection probability with unsampled years = 0

for(i in missing){
p[i] = 0; // set unsampled p to 0
}

for(i in observed){
p[i] = real_p; // fill in sampled p with a real estimate
}

for(k in 1:n_occasions - 1){
tr[1,k,1] = phi;
tr[1,k,2] = (1 - phi);
tr[2,k,1] = 0;
tr[2,k,2] = 1;

pmat[1,k,1] = p[k];
pmat[1,k,2] = (1 - p[k]);
pmat[2,k,1] = 0;
pmat[2,k,2] = 1;
}
}

model{
vector[2] pz[n_occasions]; // marginalized likelihoods

for(i in 1:NsumCH){
pz[sumf[i],1] = 1;
pz[sumf[i],2] = 0;

for(k in (sumf[i] + 1):n_occasions){
pz[k,1] = pz[k-1,1] * tr[1,k-1,1] * pmat[1,k-1,sumCH[i,(k)]];
pz[k,2] = (pz[k-1, 1] * tr[1,k-1,2] + pz[k-1, 2]) * pmat[2,k-1,sumCH[i,(k)]];
}

target += sumFR[i] * log(sum(pz[n_occasions]));

}

}

generated quantities {
vector[n_lik] log_lik;
int counter;
counter = 1;
for(i in 1:NsumCH){ // doesn't include critters observed first on last occasion
if (sumf[i] >= n_occasions){
}
else{
for(j in sumf[i]+1:n_occasions){
for(k in 1:sumFR[i]){
log_lik[counter] = bernoulli_lpmf(CH[i,j]|phi*p[j-1]);
counter += 1;
}
}
}
}
}
``````

and here is the time-dependent model [phi(t)p(.)]:

``````
data{
int<lower = 1> NsumCH; // number of capture histories
int<lower = 1> n_occasions; // number of capture occasions
int<lower = 1, upper = 2> sumCH[NsumCH, n_occasions]; // capture histories (1,2)
int<lower = 1> sumf[NsumCH]; // first capture occasion
int<lower = 1> sumFR[NsumCH]; // frequency of each capture history
int<lower = 1> n_missing; // number of unsampled years
int<lower = 1> missing[n_missing]; // unsampled years
int<lower = 1> n_observed; // number of sampled years
int<lower = 1> observed[n_observed]; // sampled years
vector[n_occasions-1] temp; // temperature each year
vector[n_occasions-1] prec; // precipitation each year
int n_lik; // number of data to calculate a likelihood for
int<lower = 0, upper = 1> CH[NsumCH, n_occasions]; // capture histories (1,0)
}

parameters{
real<lower=0,upper=1> phi[n_occasions-1]; // survival probability
real<lower = 0, upper = 1> real_p; // detection probability for all years
}

transformed parameters{
simplex[2] tr[2,n_occasions - 1]; // survival likelihoods for marginalization
simplex[2] pmat[2,n_occasions - 1]; // detection likelihoods for marginalization
// real mu[n_occasions-1];
real<lower = 0, upper = 1> p[n_occasions - 1]; // detection probability with unsampled years = 0

for(i in missing){
p[i] = 0; // set unsampled p to 0
}

for(i in observed){
p[i] = real_p; // fill in sampled p with a real estimate
}

for(k in 1:n_occasions - 1){
tr[1,k,1] = phi[k];
tr[1,k,2] = (1 - phi[k]);
tr[2,k,1] = 0;
tr[2,k,2] = 1;

pmat[1,k,1] = p[k];
pmat[1,k,2] = (1 - p[k]);
pmat[2,k,1] = 0;
pmat[2,k,2] = 1;
}
}

model{
vector[2] pz[n_occasions]; // marginalized likelihoods

for(i in 1:NsumCH){
pz[sumf[i],1] = 1;
pz[sumf[i],2] = 0;

for(k in (sumf[i] + 1):n_occasions){
pz[k,1] = pz[k-1,1] * tr[1,k-1,1] * pmat[1,k-1,sumCH[i,(k)]];
pz[k,2] = (pz[k-1, 1] * tr[1,k-1,2] + pz[k-1, 2]) * pmat[2,k-1,sumCH[i,(k)]];
}

target += sumFR[i] * log(sum(pz[n_occasions]));

}

}

generated quantities {
vector[n_lik] log_lik;
int counter;
counter = 1;
for(i in 1:NsumCH){ // doesn't include critters observed first on last occasion
if (sumf[i] >= n_occasions){
}
else{
for(j in sumf[i]+1:n_occasions){
for(k in 1:sumFR[i]){
log_lik[counter] = bernoulli_lpmf(CH[i,j]|phi[j-1]*p[j-1]);
counter += 1;
}
}
}
}
}
``````