Currently going through examples volume 1 and came across an error with the dyes example.
When I try to load inits from the example it returns "this chain contains uninitialized variables. I am not sure which part of it is not right as on the first sight I see theta, tau.btw and tau.with is all specified and nothing is left out.
I am using the code directly from Examples Vol 1 under help tab. The same error happened to all three choices of priors for between-variation.
I would really appreciate any advice on the problem. Thanks in advance.
Below is the code I copied directly from the dyes example.
model
{
for( i in 1 : batches ) {
mu[i] ~ dnorm(theta, tau.btw)
for( j in 1 : samples ) {
y[i , j] ~ dnorm(mu[i], tau.with)
}
}
theta ~ dnorm(0.0, 1.0E-10)
# prior for within-variation
sigma2.with <- 1 / tau.with
tau.with ~ dgamma(0.001, 0.001)
# Choice of priors for between-variation
# Prior 1: uniform on SD
#sigma.btw~ dunif(0,100)
#sigma2.btw<-sigma.btw*sigma.btw
#tau.btw<-1/sigma2.btw
# Prior 2: Uniform on intra-class correlation coefficient,
# ICC=sigma2.btw / (sigma2.btw+sigma2.with)
ICC ~ dunif(0,1)
sigma2.btw <- sigma2.with *ICC/(1-ICC)
tau.btw<-1/sigma2.btw
# Prior 3: gamma(0.001, 0.001) NOT RECOMMENDED
#tau.btw ~ dgamma(0.001, 0.001)
#sigma2.btw <- 1 / tau.btw
}
Data
list(batches = 6, samples = 5,
y = structure(
.Data = c(1545, 1440, 1440, 1520, 1580,
1540, 1555, 1490, 1560, 1495,
1595, 1550, 1605, 1510, 1560,
1445, 1440, 1595, 1465, 1545,
1595, 1630, 1515, 1635, 1625,
1520, 1455, 1450, 1480, 1445), .Dim = c(6, 5)))
Inits1
list(theta=1500, tau.with=1, sigma.btw=1)
Inits2
list(theta=1500, tau.with=1,ICC=0.5)
Inits3
list(theta=1500, tau.with=1, tau.btw=1)
That is not an error per se. Yes you have provided the inits for the parameters of interest.
However there are the six mu[i] variables that are not data, but are variables drawn from mu[i] ~ dnorm(theta, tau.btw).
You could provide initial values for these as well, but it is best imo to just click on gen inits if you are using WinBUGS from the GUI - this will provide initial values for those.
Related
I have a dataset similar to the one in the code below. The response variable is binary and the two predictor variables are categorical (one is binary and the other has four categories). I have created a candidate set of models, and I want to find the model with the lowest AIC in the candidate set. However, I get two error messages when running the models.
I think the problem is that it is not possible to build a spline due to the small data available or the fewer combinations of categories across the two predictor variables.
Is there a way of analysing my data using GAMs (i.e. overcoming the errors below)?
library(mgcv)
set.seed(123)
# Dummy data
dat <- data.frame(resp = sample(c(0, 1), 280, replace = T, prob = c(0.8, 0.2)),
pre1 = sample(c(0, 1), 280, replace = T, prob = c(0.6, 0.4)),
pre2 = factor(sample(c("none", "little", "some", "plenty"), 280, replace = T,
prob = c(0.25, 0.25, 0.15, 0.35))))
# Define candidate set of models
m1 <- gam(resp ~ 1, method = "REML", data = dat)
m2 <- gam(resp ~ s(pre1, k = 2), method = "REML", data = dat)
Error in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots) :
A term has fewer unique covariate combinations than specified maximum degrees of freedom
In addition: Warning message:
In smooth.construct.tp.smooth.spec(object, dk$data, dk$knots) : basis dimension, k, increased to minimum possible
m3 <- gam(resp ~ s(pre2, k = 2), method = "REML", data = dat)
Error in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots) :
NA/NaN/Inf in foreign function call (arg 1)
In addition: Warning messages:
1: In mean.default(xx) : argument is not numeric or logical: returning NA
2: In Ops.factor(xx, shift[i]) : ‘-’ not meaningful for factors
3: In smooth.construct.tp.smooth.spec(object, dk$data, dk$knots) : basis dimension, k, increased to minimum possible
m4 <- gam(resp ~ s(pre1, k = 2) + s(pre2, k = 2), method = "REML", data = dat)
m5 <- gam(resp ~ s(pre1, k = 2) * s(pre2, k = 2), method = "REML", data = dat)
# Calculate AICs
AIC(m1, m2, m3, m4, m5)
I have a dataset including three variables including company id (there are 96 companies), expert id (there are 38 experts) and points given by experts to companies. Points are discrete values from 0 to 100. I tried fitting an overdispersed poisson to model points given by the experts. But I don't know why the model overfits although I am using a linear likelihood. Here is my JAGS code:
model_code <- "
model
{
# Likelihood
for (i in 1:N) {
y[i] ~ dpois(exp(mu[i]))
mu[i] ~ dnorm(alpha[company[i]] + beta[expert[i]] , sigma^-2)
}
# Priors
for (j in 1:J){
alpha[j] ~ dnorm (mu.a, sigma.a^-2)
}
for (k in 1:K){
beta[k] ~ dnorm (mu.a, sigma.a^-2)
}
mu.a ~ dunif (0, 100)
sigma.a ~ dunif (0, 100)
sigma ~ dunif(0, 100)
}
"
Anyone knows why this model overfits and how to fix it?
I am having trouble with setting up a multivariate normal (MANOVA-like) regression in OpenBUGS on R version 3.3.2 (2016-10-31), Platform: x86_64-pc-linux-gnu (64-bit), Running under: Ubuntu 14.04.5 LTS.
My model is:
model
{
### Likelihood
for(i in 1:nfish){
CC[i, 1:ngen] ~ dmnorm(mu[i, 1:ngen], Sigma.inv[1:ngen, 1:ngen])
for(j in 1:ngen){
mu[i, j] <- CC.y[i] + beta0[j] + beta1[j] * Sdl[i]
}
}
### Priors
for(i in 1:ngen){
beta0[i] ~ dnorm(0.0, 0.01)
beta1[i] ~ dnorm(0.0, 0.01)
}
Sigma.inv[1:ngen, 1:ngen] ~ dwish(R[,], ngen)
Sigma[1:ngen, 1:ngen] <- inverse(Sigma.inv[,])
}
Data sample:
bugs.data <- list(CC = matrix(c(-3.75, -4.40, -1.60,
-3.75, -4.41, -1.71,
-8.67, NA, -5.23,
-8.92, -9.29, -5.12,
-8.69, -9.22, -9.48,
-9.87, -9.80, -18.32,
-4.71, -5.01, NA,
-4.69, -5.09, -1.67,
-3.62, -2.86, -0.26,
-3.93, -2.88, -0.21), ncol = 3),
CC.y = c(0.77, 0.82, 0.12, 0.08, 1.69, 1.69, -1.20, -1.15, 1.45, 1.46),
Sdl = c(0.96, 0.96, 0.76, 0.76, 0.40, 0.40, 0.97, 0.97, 0.27, 0.27),
nfish = 10,
ngen = 3,
R = matrix(c(3,0,0,0,3,0,0,0,3),ncol = 3));
Inits (not used since error occurs while compiling the model):
inits <- function() {list(beta0 = rnorm(3, -3.0, 1.0), beta1 = rnorm(3, 1.0, 0.2), Sigma.inv = matrix(rep(runif(1, 0, 1), 9), ncol = 3))}
Then when starting sampling:
> modelCheck("MVNmodel.bugs")
model is syntactically correct
> modelData("data.txt")
data loaded
> modelCompile(numChains = 3)
Error in handleRes(res) :
Internal "trap" error in OpenBUGS, or non-existent module or procedure called.
I get the same problem with interfacing openBUGS from R using BRugs or R2OpenBUGS. This is also not a problem of having NAs in the data. Note that the multivariate normal "Jaws" example from Lunn et al. (Jaws example) runs fine on my machine.
I am aware that this might be a hard problem to solve (See here), but I still hope for someone being able to help.
Thanks!
I'm trying to parameterise a gamma distribution in JAGS - with a piecewise linear predictor but my model fails to run with the following error message:
Error: Error in node (ashape/(aexp(mu[59]))) Invalid parent values
The model works when timber.recovery is drawn from a normal distribution, but the lower quantile predictions is less than zero, which is not biologically possible. I've tried a few tricks like adding 0.001 to the "mu" parameter in case it was drawing a zero, setting initial values based on outputs from a glm; but neither resolves the error message. any insights would be greatly appreciated [i'm using R2jags]. My model:
cat (
"model {
# UNINFORMATIVE PRIORS
sd_plot ~ dunif(0, 100)
tau_plot <- 1/(sd_plot * sd_plot)
# precision for plot level variance
alpha ~ dnorm(0, 1e-06)
# normal prior for intercept term
shape ~ dunif(0, 100)
# shape parameter for gamma
log_intensity ~ dnorm(0, 1e-06)
# uninformative prior for logging intensity
beta_1 ~ dnorm (0, 1e-06)
# uninformative prior; change in slope for first segment : <=3.6 years
beta_2 ~ dnorm (0, 1e-06)
# uninformative prior; change in slope for first segment : >3.6 years
InX_1 ~ dnorm (0, 1e-06)
# uniformative prior for interaction between tsl and log_intensity : <=3.6 years
InX_2 ~ dnorm (0, 1e-06)
# uniformative prior for interaction between tsl and log_intensity : >3.6 years
# PLOT LEVEL RANDOM EFFECTS
for (i in 1:nplots) {
plot_Eff[i] ~ dnorm(0,tau_plot)
}
for (i in 1:Nobs) {
# PIECEWISE LINEAR PREDICTOR
mu[i] <-
alpha +
beta_1 * (time.since.logged[i] * tsl.DUM1[i]) +
log_intensity * log.volume [i] +
beta_2 * (time.since.logged[i] * tsl.DUM2[i] - 3.6) +
beta_1 * (time.since.logged[i] * tsl.DUM2[i]) +
plot_Eff[plot.id[i]] +
InX_1 * (time.since.logged[i] * tsl.DUM1[i]) * log.volume [i] +
InX_2 * (time.since.logged[i] * tsl.DUM2[i] - 3.6) * log.volume[i] +
InX_1 * (time.since.logged[i] * tsl.DUM2[i]) * log.volume[i]
timber.recovery[i] ~ dgamma(shape,shape/exp(mu[i]))
# observed recovery
pred_timber_recovery[i] ~ dgamma(shape,shape/exp(mu[i]))
# posterior predictive distribution
pearson.residual[i] <-
(timber.recovery[i] - pred_timber_recovery[i]) / (sqrt(timber.recovery[i]))
}
}",
fill = TRUE,
file = "outputs/piecewise_TIMBER_MODEL_FINAL_GAMMA.txt")
I am having a problem in initializing the following model in OpenBUGS
model
{
#likelihood
for (t in 1:n) { yisigma2[t] <- 1/exp(theta[t]);
y[t] ~ dnorm(0,yisigma2[t]);
}
#Priors
mu ~ dnorm(0,0.1);
phistar ~ dbeta(20,1.5);
itau2 ~ dgamma(2.5,0.025);
beta <- exp(mu/2);
phi <- 2*phistar-1;
tau <- sqrt(1/itau2);
theta0~dnorm(mu, itau2)
thmean[1] <- mu + phi*(theta0-mu);
theta[1] ~ dnorm(thmean[1],itau2);
for (t in 2:n) { thmean[t] <- mu + phi*(theta[t-1]-mu);
theta[t] ~ dnorm(thmean[t],itau2);
}
}
This is my data
list(y=c(-0.0383 , 0.0019 ,......-0.0094),n=945)
And this is the list of my initials
list(phistar= 0.98, mu=0, itau2=50)
The checking of model, loading of data and compilation steps are ok. When loading initials, OpenBUGS says initial values are loaded but chain contains uninitialized variables. I then tried to initialize theta0 also but the problem persists. Could someone please help me regarding this?
Thanks
Khalid
I am newbie at OpenBugs but shouldn't you be specifying a distribution for inits rather than a single point value? something like?
inits <- function(){ list(alpha=rnorm(1), beta=rnorm(1), sigma = rlnorm(1))}