hi all . i am trying to run the bym2 model . at this stage, i have a problem.are you help me? - geospatial

Question

library(“rstan”)
library(“rstudioapi”)
library(“parallel”)
library(“brms”)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectgores())
library(pkgbuild) # load packAge
find_rtools() # should be TRUE, assuming you have Rtools 3.5
#fit model icar.stan to NYC census tracts neighborhood map
install.packages(‘tidyverse’, dependencies = TRUE)
install.packages(‘rstanarm’, dependencies = TRUE)
library(rstan);
library(tidyverse)
library(rstanarm)
"data {
int<lower=0> N;
int<lower=0> N_edges;
int<lower=1, upper=N> node1[N_edges]; // node1[i] adjacent to node2[i]
int<lower=1, upper=N> node2[N_edges]; // and node1[i] < node2[i]
int<lower=0> y[N]; // count outcomes
vector<lower=0>[N] E; // exposure
int<lower=1> K; // num covariates
matrix[N, K] x; // design matrix
real<lower=0> scaling_factor; // scales the variance of the spatial effects
}
transformed data {
vector[N] log_E = log(E);
}
parameters {
real beta0; // intercept
vector[K] betas; // covariates
real<lower=0> sigma; // overall standard deviation
real<lower=0, upper=1> rho; // proportion unstructured vs. spatially structured variance
vector[N] theta; // heterogeneous effects
vector[N] phi; // spatial effects
}
transformed parameters {
vector[N] convolved_re;
// variance of each component should be approximately equal to 1
convolved_re = sqrt(1 - rho) * theta + sqrt(rho / scaling_factor) * phi;
}
model {
y ~ poisson_log(log_E + beta0 + x * betas + convolved_re * sigma); // co-variates
// This is the prior for phi! (up to proportionality)
target += -0.5 * dot_self(phi[node1] - phi[node2]);
beta0 ~ normal(0.0, 1.0);
betas ~ normal(0.0, 1.0);
theta ~ normal(0.0, 1.0);
sigma ~ normal(0, 1.0);
rho ~ beta(0.5, 0.5);
// soft sum-to-zero constraint on phi)
sum(phi) ~ normal(0, 0.001 * N); // equivalent to mean(phi) ~ normal(0,0.001)
}
generated quantities {
real logit_rho = log(rho / (1.0 - rho));
vector[N] eta = log_E + beta0 + x * betas + convolved_re * sigma; // co-variates
vector[N] mu = exp(eta);
}"
options(mc.cores = parallel::detectCores())
library(INLA)
source(“mungecardata4stan.R”)
source(“iran_data.R”)
y = data$y;
E = data$E;
K = 1;
x = 0.1 * data$x;
nbs = mungeCARdata4stan(data$adj, data$num);
N = nbs$N;
node1 = nbs$node1;
node2 = nbs$node2;
N_edges = nbs$N_edges;
adj.matrix = sparseMatrix(i=nbs$node1,j=nbs$node2,x=1,symmetric=TRUE)
Q= Diagonal(nbs$N, rowSums(adj.matrix)) - adj.matrix
Q_pert = Q + Diagonal(nbs$N) * max(diag(Q)) * sqrt(.Machine$double.eps)
Q_inv = inla.qinv(Q_pert, constr=list(A = matrix(1,1,nbs$N),e=0))
scaling_factor = exp(mean(log(diag(Q_inv))))
scot_stanfit = stan(“bym2_predictor_plus_offset.stan”, data=list(N,N_edges,node1,node2,y,x,E,scaling_factor), warmup=5000, iter=6000);
Error in new_CppObject_xp(fields$.module, fields$.pointer, …) : **
** Exception: variable does not exist; processing stage=data initialization; variable name=N; base type=int (in ‘string’, line 3, column 2 to column 17)
In addition: Warning message:
In readLines(file, warn = TRUE) :
** incomplete final line found on ‘C:\Users\Uaer\Downloads\bym2_predictor_plus_offset.stan’
failed to create the sampler;** sampling not done

Answer 1

in my opinion , in source(“mungecardata4stan.R”) you should type the address of mungecardata4stan.R that is placed in your pc. and also for source(“iran_data.R”). like this: source("C:/Users/me/Desktop/iran_data.R").