######################################################################
## Simulation and estimation of Exponential Random Partition Models ##
## Functions to plot the estimation algorithm results ##
## Author: Marion Hoffman ##
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print_results <- function(result){
num.effects <- length(result$effect)
effect <- result$effect
object <- result$object
est <- result$est
std.err <- result$std.err
conv <- result$conv
t <- est / std.err
sig <- rep("", num.effects)
sig[abs(t) > qnorm(1 - 0.05/2)] <- "*"
sig[abs(t) > qnorm(1 - 0.01/2)] <- "**"
sig[abs(t) > qnorm(1 - 0.001/2)] <- "***"
print( data.frame(effect, object, est, std.err, sig, t, conv) )
}
print_results_bayesian <- function(result){
num.effects <- length(result$effect)
effect <- result$effect
object <- result$object
post.mean <- result$post.mean
post.sd <- result$post.sd
cred.min <- post.mean - 1.95996*post.sd
cred.max <- post.mean + 1.95996*post.sd
print( data.frame(effect, object, post.mean, post.sd, cred.min, cred.max) )
}
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