brms (version 2.6.0)

pp_mixture.brmsfit: Posterior Probabilities of Mixture Component Memberships

Description

Compute the posterior probabilities of mixture component memberships for each observation including uncertainty estimates.

Usage

# S3 method for brmsfit
pp_mixture(x, newdata = NULL, re_formula = NULL,
  resp = NULL, nsamples = NULL, subset = NULL, log = FALSE,
  summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ...)
pp_mixture(x, ...)

Arguments

x

An R object usually of class brmsfit.

newdata

An optional data.frame for which to evaluate predictions. If NULL (default), the original data of the model is used.

re_formula

formula containing group-level effects to be considered in the prediction. If NULL (default), include all group-level effects; if NA, include no group-level effects.

resp

Optional names of response variables. If specified, predictions are performed only for the specified response variables.

nsamples

Positive integer indicating how many posterior samples should be used. If NULL (the default) all samples are used. Ignored if subset is not NULL.

subset

A numeric vector specifying the posterior samples to be used. If NULL (the default), all samples are used.

log

Logical; Indicates whether to return probabilities on the log-scale.

summary

Should summary statistics (i.e. means, sds, and 95% intervals) be returned instead of the raw values? Default is TRUE.

robust

If FALSE (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If TRUE, the median and the median absolute deviation (MAD) are applied instead. Only used if summary is TRUE.

probs

The percentiles to be computed by the quantile function. Only used if summary is TRUE.

...

Further arguments passed to extract_draws that control several aspects of data validation and prediction.

Value

If summary = TRUE, an N x E x K array, where N is the number of observations, K is the number of mixture components, and E is equal to length(probs) + 2. If summary = FALSE, an S x N x K array, where S is the number of posterior samples.

Details

The returned probabilities can be written as \(P(Kn = k | Yn)\), that is the posterior probability that observation n originates from component k. They are computed using Bayes' Theorem $$P(Kn = k | Yn) = P(Yn | Kn = k) P(Kn = k) / P(Yn),$$ where \(P(Yn | Kn = k)\) is the (posterior) likelihood of observation n for component k, \(P(Kn = k)\) is the (posterior) mixing probability of component k (i.e. parameter theta<k>), and $$P(Yn) = \sum (k=1,...,K) P(Yn | Kn = k) P(Kn = k)$$ is a normalizing constant.

Examples

# NOT RUN {
## simulate some data
set.seed(1234)
dat <- data.frame(
  y = c(rnorm(100), rnorm(50, 2)), 
  x = rnorm(150)
)
## fit a simple normal mixture model
mix <- mixture(gaussian, nmix = 2)
prior <- c(
  prior(normal(0, 5), Intercept, nlpar = mu1),
  prior(normal(0, 5), Intercept, nlpar = mu2),
  prior(dirichlet(2, 2), theta)
)
fit1 <- brm(bf(y ~ x), dat, family = mix,
            prior = prior, chains = 2, inits = 0)
summary(fit1)
   
## compute the membership probabilities         
ppm <- pp_mixture(fit1)
str(ppm)

## extract point estimates for each observation
head(ppm[, 1, ])

## classify every observation according to 
## the most likely component
apply(ppm[, 1, ], 1, which.max)
# }
# NOT RUN {
# }