R JAGS: Dimension mismatch - r
I am trying to run a model in JAGS but I got the following error:
Error in jags.model(model.file, data = data, inits = init.values, n.chains = n.chains, :
RUNTIME ERROR:
Cannot insert node into m[1]. Dimension mismatch
The model I specified is
model = "model
{
for (i in 1:N) {
z[i] ~ dnorm(m[i],tau)
m[i] <- beta0 + beta1*x + beta2*y
}
beta0 ~ dnorm(0.0,1.0E-3)
beta1 ~ dnorm(0.0,1.0E-3)
beta2 ~ dnorm(0.0,1.0E-3)
tau ~ dgamma(0.01,0.01)
}"
Then I specify the data and the initial values. Finally, I run the model.
data = list(z = wolfcamp$data, x = wolfcamp$coords[,1], y = wolfcamp$coords[,2], N = length(wolfcamp$data))
parameters=c("beta0","beta1","beta2","tau")
init1 = list(beta0 = 0, beta1 = 0, beta2 = 0, tau = 0)
init2 = list(beta0 = 1, beta1 = 1, beta2 = 1, tau = 1)
initial.values=list(init1, init2)
model1=jags(data=data, inits=initial.values,
parameters.to.save=parameters,
model.file=textConnection(model),
n.chains=2,
n.burnin=1000,
n.iter=11000)
Can you help me? Do you know why I get this error?
Thanks in advance.
It looks like you are not indexing through the vectors x and y, which causes a dimension mismatch.
Change this line:
m[i] <- beta0 + beta1*x + beta2*y
to this:
m[i] <- beta0 + beta1*x[i] + beta2*y[i]
and you should be good to go (so long as x and y are of length N)
Related
Compilation Error in jags.model: Runtime Error: Index out of range while running linear mixed model
I am trying to run linear mixed model in JAGS in R.
I am trying to see influence of year as a random factor on speciesTotal.
My code is:
data_glmm<-read.csv("data_bbs_11_19.csv")
Nyear<-length(levels(as.factor(data_glmm$Year)))
Nyear<-9
jagsData_glmm<-with(data_glmm,
list(SpeciesTotal=SpeciesTotal,
StopTotal=StopTotal,
Stratum=Stratum,
Year=Year, N=length(SpeciesTotal), Nyear=Nyear))
lm1_jags <- function() {
for (i in 1:N){
SpeciesTotal[i] ~ dnorm(mu[i], tau) # tau is precision (1 / variance)
mu[i] <- alpha + a[Year[i]] + beta * StopTotal[i] # Random intercept for year
}
# Priors:
alpha ~ dnorm(0, 0.01) # intercept
sigma_a ~ dunif(0, 100) # standard deviation of random effect (variance between year)
tau_a <- 1 / (sigma_a * sigma_a) # convert to precision
for (j in 1:9){
a[j] ~ dnorm(0, tau_a) # random intercept for each year
}
beta ~ dnorm(0, 0.01) # slope
sigma ~ dunif(0, 100) # standard deviation of fixed effect (variance within year)
tau <- 1 / (sigma * sigma) # convert to precision
}
init_values <- function(){
list(alpha = rnorm(1), sigma_a = runif(1), beta = rnorm(1), sigma = runif(1))
}
params <- c("alpha", "beta", "sigma", "sigma_a")
fit_lm3 <- jags(data = jagsData_glmm, inits = init_values, parameters.to.save = params, model.file = lm1_jags, n.chains = 3, n.iter = 20000, n.burnin = 5000, n.thin = 10, DIC = F)
While running this code, I am getting following error:
Error in jags.model(model.file, data = data, inits = init.values, n.chains = n.chains, :
RUNTIME ERROR:
Compilation error on line 5.
Index out of range taking subset of a
Any suggestion would be really appreciated.
Problem adjusting a linear model with JAGS
I am trying to adjust a linear model with JAGS but I'm having trouble with the code. I'm writing:
library(R2jags)
library(BEST)
base<-data.table::data.table(read.csv("/Users/franco/Documents/Todo/UNAM/Facultad\ de\ Ciencias/Asignaturas\ Actuaría/Análisis\ Bayesiano\ de\ Datos/Tareas/Tarea-Examen\ 2/FootballLeague.csv"))
X <- cbind(1,as.matrix(base[,-c(1,2,12)]))
y <-as.matrix(base[,2])
n <- length(y)
m <- ncol(X)
model.jags <- function(){
tau ~ dgamma(0.01, 0.01)
for(i in 1:m){
beta[i] ~ dnorm(0,0.001)
}
for (i in 1:n){
y[i] ~ dnorm(x[i,]%*%beta,tau)
}
sigma <- pow(tau,-1)
}
jags.params <- c("beta","sigma")
jags.modelo <- jags(model.file=model.jags,parameters.to.save=jags.params,
data = list('n' = n,
'y' = y,
'x' = X,
'm'=m),
n.chains = 2,
n.thin=1,
DIC=FALSE,
n.burnin = 10000,
n.iter = 20000)
And R throws this error:
Error in jags.model(model.file, data = data, inits = init.values,
n.chains = n.chains, : RUNTIME ERROR: Compilation error on line 8.
Dimension mismatch in subset expression of y.
I don't know which is the error :/ Can someone help me, please.
Rstan Error: Cannot coerce type 'closure' to vector of type 'character'
I'm following the blog post here (https://mattocci27.github.io/assets/poilog_mix/poilog_mix.html) exactly, but keep running into a strange error. To be totally precise, I am running:
library(rstan)
mu <- c(log(50), log(400))
sigma <- c(0.8, 0.2)
theta <- 0.8
N <- 100
z <- rbinom(N, 1, 1 - theta) + 1
log_lambda <- rnorm(N, mu[z], sigma[z])
Y <- rpois(N, exp(log_lambda))
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
list_dat <- list(N = N, y = Y)
fit_cp <- stan(file = "poilog_mix_cp.stan",
data = list_dat,
iter = 2000,
warmup = 1000,
thin = 1,
chains = 4,
refresh = 500,
control = list(adapt_delta = 0.9, max_treedepth = 20))
with the file "poilog_mix_cp.stan" as follows:
data{
int<lower=0> N;
int y[N];
}
parameters {
ordered[2] mu;
real<lower=0> sigma[2];
real<lower=0, upper=1> theta;
vector[N] log_lambda[2];
}
model {
sigma ~ cauchy(0, 2.5);
mu ~ normal(0, 10);
theta ~ beta(2, 2);
for (i in 1:2)
log_lambda[i,] ~ normal(mu[i], sigma[i]);
for (n in 1:N)
target += log_mix(theta,
poisson_log_lpmf(y[n] | log_lambda[1, n]),
poisson_log_lpmf(y[n] | log_lambda[2, n]));
}
but at the step of fitting the model, I get the following error:
Error in as.character(sys.call(sys.parent())[[1L]]) :
cannot coerce type 'closure' to vector of type 'character'
I'm not sure what's going on, since I'm just copying the blog post.
Bayesian Beta regression Model -- Error in jags: Invalid parent value
I'm trying to run a Bayesian pooled model in jags through R and getting an error message
I found from people who have encountered similar problems that it could be triggered by values of the priors, negative value, log of negative, syntax errors etc. I have eliminated all of these but the error persists.
## just for the prediction
pred.jac <- seq(min(test.bayes$Latitude), max(test.bayes$Latitude), 10)
data = list(
jac = test.bayes$Jaccard,
lat = test.bayes$Latitude,
pred.jac = pred.jac)
inits = list(
list(alpha = 1, beta = 2.5, sigma = 50),
list(alpha = 2, beta = 1.5, sigma = 20),
list(alpha = 3, beta = 0.75, sigma = 10))
{
sink("BetaPooledJAGS.R")
cat("
model{
# priors
alpha ~ dnorm(0, 0.0001)
beta ~ dnorm(0, 0.0001)
sigma ~ dunif(0, 10)
# likelihood
for (i in 1:length(jac)) {
mu[i] <- alpha + beta * lat[i]
a[i] <- ((1 - mu[i]) / (sigma^2) - 1 / mu[i]) * mu[i]^2
b[i] <- alpha * (1 / mu[i] - 1)
jac[i] ~ dbeta(a[i], b[i])
}
# predicted jaccard as derived quantities
for (i in 1:length(pred.jac)) {
mu_pred[i] <- alpha + beta * lat[i]
mu_pred1[i] <- exp(mu_pred[i])
}
}
",fill = TRUE)
sink()
}
n.adapt = 3000
n.update = 5000
n.iter = 5000
jm.pooled = jags.model(file="BetaPooledJAGS.R", data = data, n.adapt = n.adapt, inits = inits, n.chains = length(inits))
When I run the code, I get the error below:
Error in jags.model(file = "BetaPooledJAGS.R", data = data, n.adapt = n.adapt, : Error in node jac[1] Invalid parent values
Here's the link to a subset of my data.
https://fil.email/IuwgYhKs
You're getting negative values for b with those initials if lat is positive, and b must be > 0 in a beta distribution, in JAGS, and more generally. E.g. using initials from inits[[1]]: mu = 1 + 2.5*lat assuming lat is positive, then mu > 1 b = 1 * (1/mu-1) And 1/mu < 1 if mu>1, so 1/mu - 1 < 0. Therefore b = 1*-ve so b<0
Censoring in rjags - Invalid parent values
I'm having troubles reimplementing a model from winbugs on rjags. I'm getting the Invalid parent values error which is the error you get when censoring was not correctly setup, but I can't see my mistake.
This is the original model on WinBugs:
model {
for(i in 1 : N) {
times[i] ~ dweib(v, lambda[i]) T(censor[i],)
lambda[i] <- exp(beta0 + beta1*type[i])
S[i] <- exp(-lambda[i]*pow(times[i],v));
f[i] <- lambda[i]*v*pow(times[i],v-1)*S[i]
h[i] <- f[i]/S[i]
}
beta0 ~ dnorm(0.0, 0.0001)
beta1 ~ dnorm(0.0, 0.0001)
v ~ dexp(0.001)
median0 <- pow(log(2) * exp(-beta0), 1/v)
median1 <- pow(log(2) * exp(-beta0-beta1), 1/v)
}
Setting up a reproducible example:
type <- as.factor(c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0))
censor <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,882,892,1031,
1033,1306,1335,0,1452,1472,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,381,0,0,0,0,0,0,0,0,0,529,0,
0,0,0,0,0,0,0,0,945,0,0,1180,0,0,1277,1397,1512,1519)
times <-c (17,42,44,48,60,72,74,95,103,108,122,144,167,170,183,185,193,195,197,208,234,235,254,307,315,401,
445,464,484,528,542,567,577,580,795,855,NA,NA,NA,NA,NA,NA,1366,NA,NA,1,63,105,129,182,216,250,262,
301,301,342,354,356,358,380,NA,383,383,388,394,408,460,489,499,524,NA,535,562,675,676,748,748,778,
786,797,NA,955,968,NA,1245,1271,NA,NA,NA,NA)
df <- tibble(type = type, censor = censor, time = times) %>%
mutate(censor_limit = replace(censor, censor == 0, max(times, na.rm = TRUE))) %>%
mutate(is_censored = ifelse(is.na(time), 1, 0)) %>%
mutate(time_init = ifelse(is_censored == 1, censor_limit + 1, NA))
df$censor <- NULL
head(df)
And this is the rjags part:
m <- textConnection("model {
for(i in 1 : N) {
isCensored[i] ~ dinterval(times[i], censorLimit[i])
times[i] ~ dweib(v, lambda[i])
lambda[i] <- exp(beta0 + beta1*type[i])
S[i] <- exp(-lambda[i]*pow(times[i],v));
f[i] <- lambda[i]*v*pow(times[i],v-1)*S[i]
h[i] <- f[i]/S[i]
}
beta0 ~ dnorm(0.0, 0.0001)
beta1 ~ dnorm(0.0, 0.0001)
v ~ dexp(0.001)
# Median survival time
median0 <- pow(log(2) * exp(-beta0), 1/v)
median1 <- pow(log(2) * exp(-beta0-beta1), 1/v)
}")
d <- list(N = nrow(df), times = df$time, type = df$type, isCensored = df$is_censored,
censorLimit = df$censor_limit)
inits1 = function() {
inits = list(v = 1, beta0 = 0, beta1=0, times = df$time_init)
}
mod <- jags.model(m, data = d, inits = inits1, n.chains = 3)
update(mod, 1e3)
mod_sim <- coda.samples(model = mod, variable.names = c("lambda", "median0", "median1"), n.iter = 5e3)
mod_csim <- as.mcmc(do.call(rbind, mod_sim))
Output:
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 164
Unobserved stochastic nodes: 19
Total graph size: 910
Initializing model
Deleting model
Error in jags.model(m, data = d, inits = inits1, n.chains = 3): Error in node h[35]
Invalid parent values