# Bayesian p-value in wrong direction using step function in JAGS / BUGS

I have estimated a Weibull regression model in JAGS using rjags and R2JAGS.

The estimated posterior predictive p-values using the step() function confuse me. They make sense (comparing them to lower and upper bound) when the estimated regression coefficients are negative but seem wrong, and almost like 1-p would be correct, when reg coefficients are estimated to be positive.

Is that just how the step function works? The thing is, in examples, I only see step functions implemented like: ppp.b.0 <- step(b.0) and never like ppp.b.0 <- 1-step(b.0) if b.0>0.

This is the result: And this is the model:

modelstring.sl <- function() {
for (i in 1:n.G) {
censored[i] ~ dinterval(t[i], t.cen[i])
t[i] ~ dweib(r, mu[i]) # survival time ~ dweib(shape = r, scale = mu[i])
log(mu[i]) <- b.0 + inprod(X.SL[i,], b.SL)
}

# priors
b.0 ~ dnorm(0.0, 0.0001) # intercept

for (x in 1:n.XSL) {
b.SL[x]  ~ dnorm(0.0, 0.0001) # reg coefs
}

r ~ dexp(0.001) # shape parameter

var.G <- 1/(3.142^2/(6 *exp(2*log(r)))) # variance

# ppp-values
for (x in 1:n.XSL) {
ppp.b.SL[x] <- step(b.SL[x])
}
ppp.b.0 <- step(b.0)
}

# Data: character vector of variable names #
jags.data   <- c("t", "t.cen", "censored", "n.G", "X.SL", "n.XSL")

# Parameters: character vector of parameters to be monitored #
jags.params <- c("r", "var.G", "b.0", "b.SL", "ppp.b.SL", "ppp.b.0")

# Initial values in list form #
t.0 <- t
t.0[censored==0] <- NA
t.0[censored==1] <- t.cen[censored==1] + 5

jags.inits <- list(
list(t=t.0, b.0=0.0, b.SL=rep( 0.1,n.XSL), r=1.1),
list(t=t.0, b.0=0.1, b.SL=rep(-0.1,n.XSL), r=1.2),
list(t=t.0, b.0=0.2, b.SL=rep( 0.0,n.XSL), r=1.3))

# Run model with R2JAGS
jagsfit.sl3 <- jags(data=jags.data, inits = jags.inits, parameters.to.save = jags.params, n.chains = 3, n.iter = iter, n.burnin = burnin, model.file = modelstring.sl)