I’m attempting to run a MultiLogit model in RStan (using RStudio) following the code in section 1.6, page 18 of the Stan users Guide version 2.25. I’ve created some test data with two predictors (length and age. Ages range from 2 to 6. The outcome variable Status codes the age at which maturation occurs conditional on age and length. Immaturity (failure to mature) is coded as 1, maturity ranges from 2 to 6.
The .R, .stan, and .txt data file are attached.
When I run the simple fit command below for a total of 400 iterations or fewer the program runs, but this number of iterations is too few to achieve any credible sampling of the posterior. If I try to run, say, at total of 500 iteration, the program hangs at around 280 iterations.
fit<-stan(file = 'MLTest1.stan', data = stanDat, control = list(adapt_delta = 0.99, max_treedepth=20), iter = 400, chains = 1)
So, I think I need to set some initial values, as follows:
fit<-stan(file = 'MLTest1.stan', data = stanDat,control = list(adapt_delta = 0.99, max_treedepth=20), init=list(list(beta[1,1] = 0.0,beta[1,2] = 0.0,beta[1,3] = 0.0,beta[1,4] = 0.0, beta[1,5] = 0.0, beta[1,6] = 0.0, beta[2,1] = 0.0, beta[2,2] = 0.0, beta[2,3] = 0, beta[2,4] = 0.0,beta[2,5] = 0.0, beta[2,6] = 0.0)), iter = 100, chains = 1)
But doing so using the code above produces the error message:
Error: unexpected '=' in:
"fit<-stan(file = 'MLTest1.stan', data = stanDat,control = list(adapt_delta = 0.99, max_treedepth=20),
init=list(list(beta[1,1] ="
> beta[2,1] = 0.0, beta[2,2] = 0.0, beta[2,3] = 0, beta[2,4] = 0.0,beta[2,5] = 0.0)),iter = 100, chains = 1)
Error: unexpected ',' in " beta[2,1] = 0.0,"
Isn’t it possible to set initial values for two dimensional parameter
Variables? What am I doing wrong and/or failing to understand?
Any suggestions are most welcome.
Here are the .R, .stan, and text data files:
#MLTest.R 5 age classes , 2 to 6.
#Test of multi-logit regression of probability of maturation at age and length
#5 age classes (2 to 6), 6 possible fates: immature (coded as 1), mature at ages
#2 to 6.
#NG Sept 29 2021
TestData<- read.table("TestData.txt", header = TRUE)
y<-TestData$Status #mature = 2 to 6, immature = 1
l <-TestData$Length
w<-TestData$Age #ages = 2 to 6, coded as 1 to 5
N = nrow(TestData)
D = 2; # number of predictors
K = 6; #number of outomes (= 0 to 5)
x = data.frame(l,w);
stanDat<- list(y=y, l=l, w=w, x=x,D=D, K=K, N = N);
fit<-stan(file = 'MLTest1.stan', data = stanDat, control = list(adapt_delta = 0.99, max_treedepth=20),iter = 400, chains = 1)
fit<-stan(file = 'MLTest1.stan', data = stanDat,control = list(adapt_delta = 0.99, max_treedepth=20),
init=list(list(beta[1,1] = 0.0,beta[1,2] = 0.0,beta[1,3] = 0.0,beta[1,4] = 0.0, beta[1,5] = 0.0, beta[1,6] = 0.0,
beta[2,1] = 0.0, beta[2,2] = 0.0, beta[2,3] = 0, beta[2,4] = 0.0,beta[2,5] = 0.0, beta[2,6] = 0.0)),iter = 100, chains = 1)
//MLTest1.stan
//Test program to conduct a multi-logit regression (Stan Users manual section 2.6) on
//probability of age-at-maturity given age and length
//NG started september 27 2021
data{
int<lower=2> K;
int<lower=1> D;
int<lower=0> N;
int<lower=0, upper=K> y[N];
matrix[N,D] x;
}
parameters{
matrix[D,K] beta;
}
model{
matrix[N,K] x_beta = x*beta;
to_vector(beta) ~normal(0,5);
for (n in 1:N)
y[n] ~ categorical_logit(x_beta[n]');
}
|Length|Age|Status|||
|---|---|---|---|---|
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