Compare latent class panel models with different numbers of classes

Fits panfmm() for each number of classes in k and tabulates the log-likelihood, number of parameters, AIC and BIC (with n the number of observations), the usual criteria for choosing the number of classes (Deb and Trivedi 2002 prefer BIC). Mixtures with too many classes may fail to converge or return near-empty classes; such fits are reported with NA.

Usage

panfmmselect(
  formula,
  data,
  index,
  k = 1:3,
  family = "poisson",
  concomitant = NULL,
  ...
)

Arguments

formula, data, index, family, concomitant, ...

passed to panfmm()

k

integer vector of numbers of classes

Value

a data.frame with columns k, logLik, df, AIC, BIC and smallest (the smallest mean class probability), with the fits in the attribute "fits"

Validation

Numerical accuracy is checked in the package tests and in the validation suite; see the validation article on the package website.

See also

Other panel models: panfmm(), panglm(), pangmm(), panhurdle(), paniv(), panqr(), panselect(), twopart()

Examples

set.seed(3)
d <- data.frame(id = rep(1:150, each = 3), t = rep(1:3, 150), x = rnorm(450))
d$y <- rpois(450, exp(ifelse(rep(rbinom(150, 1, 0.4), each = 3) == 1, 1.5, 0) + 0.3 * d$x))
panfmmselect(y ~ x, data = d, index = c("id", "t"), k = 1:2, nstart = 2, seed = 1)
#>   k     logLik df      AIC      BIC  smallest
#> 1 1 -1004.9506  2 2013.901 2022.120 1.0000000
#> 2 2  -799.9038  5 1609.808 1630.354 0.3791781
# \donttest{
# up to three classes (a few seconds)
panfmmselect(y ~ x, data = d, index = c("id", "t"), k = 1:3, nstart = 2, seed = 1)
#>   k     logLik df      AIC      BIC  smallest
#> 1 1 -1004.9506  2 2013.901 2022.120 1.0000000
#> 2 2  -799.9038  5 1609.808 1630.354 0.3791781
#> 3 3  -798.7190  8 1613.438 1646.312 0.1170190
# }