Hi all,

I’m new to stan.

The purpose of my analysis is to detect how the neighbor crowding influence the survival of focal tree. The number of survival observations is about 20,000.

This is some results of the model, I set iter = 500, chains = 1 and algorithm=“HMC”.

```
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
bet[1] 0.51 0.02 0.28 0.03 0.31 0.48 0.71 1.05 250 1.00
bet[2] 1.41 0.07 1.04 -0.53 0.76 1.36 2.06 3.81 250 1.00
bet[3] 9.08 6.39 101.02 -165.10 -59.97 1.92 69.82 239.96 250 1.00
bet[4] 2.67 12.49 80.92 -153.52 -57.21 -4.51 54.76 175.49 42 1.01
bet[5] 13.75 5.93 93.81 -156.52 -55.79 3.64 78.66 192.58 250 1.00
b[1,1] 1.15 0.04 0.69 -0.19 0.65 1.20 1.62 2.45 250 1.00
b[1,2] 7.02 3.66 9.03 -5.46 0.76 4.28 11.58 28.70 6 1.14
b[1,3] -0.49 1.70 4.69 -10.67 -2.88 0.35 2.74 7.77 8 1.14
b[1,4] -6.64 2.03 5.36 -19.14 -10.09 -5.39 -2.76 1.03 7 1.08
```

My questions:

- why are the n_eff so different, ranging from 6 to 250? I search for the meaning of n_eff and find nothing.
- I have tried to increase the number of iteration and chain, but the running time is quite long. Is there anything suggestion to speed up the model?

Following is the stan model I used.

```
data { #these are all data elements
int<lower=0> nplot; #number of unique plot
int<lower=0> nspe; #number of species
int<lower=0> ncensus; #number of census
int<lower=0> n; #number of survival observations
int<lower=0> ncov; #number of covariates
int<lower=0> plot[n]; #plot for each survival observation
int<lower=0> spe[n]; #species of each survival observation
int<lower=0> census[n]; #census for each survival observation
vector[nspe] meanT; #trait values for each species
matrix[n,ncov] COV; #covariate values for each observation, where first column is all equal to ‘1’ for intercept, second column is NC, third column is NCI, fourth column is NCIH, fifth column is log.DBH
int<lower=0,upper=1> status[n]; #survival observations
}
parameters {
real bet[5];
real b[nspe,ncov];
real int2[nplot];
real int3[ncensus];
real <lower=0.00001> sigmab[ncov];
real int1[ncov];
real <lower=0.00001> sigmaplot;
real <lower=0.00001> sigmacensus;
}
model {
sigmacensus~gamma(100,100);
sigmaplot~gamma(100,100);
for(i in 1:4)
{
bet[i]~normal(0,100);
}
for(i in 1:ncov)
{
int1[i]~normal(0,100);
sigmab[i]~gamma(100,100);
}
for(j in 1:nspe)
{
b[j,1]~normal(int1[1]+bet[1]*meanT[j],sigmab[1]);
b[j,2]~normal(int1[2]+bet[2]*meanT[j],sigmab[2]);
b[j,3]~normal(int1[3],sigmab[3]);
b[j,4]~normal(int1[4],sigmab[4]);
b[j,4]~normal(int1[4],sigmab[4]);
}
for(i in 1:nplot)
{
int2[i]~normal(0,sigmaplot);
}
for(i in 1:ncensus)
{
int3[i]~normal(0,sigmacensus);
}
for(i in 1:n)
{
status[i]~bernoulli_logit(b[spe[i],1]*COV[i,1]+b[spe[i],2]*COV[i,2]+b[spe[i],3]*COV[i,3]+b[spe[i],4]*COV[i,4]+b[spe[i],5]*COV[i,5]
int2[plot[i]]+
int3[census[i]]) ;
}
}
```