probitInverse: Probit-Inverse of Group-Level Normal Distribution

In TreeBUGS: Hierarchical Multinomial Processing Tree Modeling

Probit-Inverse of Group-Level Normal Distribution

Description

Transform latent group-level normal distribution (latent-trait MPT) into mean and SD on probability scale.

Usage

probitInverse(mu, sigma, fittedModel = NULL)

Arguments

mu	latent-probit mean of normal distribution
sigma	latent-probit SD of normal distribution
fittedModel	optional: fitted traitMPT model. If provided, the bivariate inverse-probit transform is applied to all MCMC samples (and mu and sigma are ignored).

Value

implied mean and SD on probability scale

Examples

####### compare bivariate vs. univariate transformation
probitInverse(mu = 0.8, sigma = c(0.25, 0.5, 0.75, 1))
pnorm(0.8)

# full distribution
prob <- pnorm(rnorm(10000, mean = 0.8, sd = 0.7))
hist(prob, 80, col = "gray", xlim = 0:1)

## Not run: 
# transformation for fitted model
mean_sd <- probitInverse(fittedModel = fit)
summarizeMCMC(mean_sd)

## End(Not run)

TreeBUGS documentation built on May 31, 2023, 9:21 p.m.