Fitted Values for sfclust Objects

This function calculates the fitted values for a specific clustering sample in an sfclust object, based on the estimated models for each cluster. The fitted values are computed using the membership assignments and model parameters associated with the selected clustering sample.

Usage

# S3 method for class 'sfclust'
fitted(object, sample = object$clust$id, sort = FALSE, aggregate = FALSE, ...)

Arguments

object

An object of class 'sfclust', containing clustering results and models.

sample

An integer specifying the clustering sample number for which the fitted values should be computed. The default is the id of the current clustering. The value must be between 1 and the total number of clustering (membership) samples.

sort

Logical value indicating if clusters should be relabel based on number of elements.

aggregate

Logical value indicating if fitted values are desired at cluster level. Only supported for sfclust_stars results.

...

Additional arguments, currently not used.

Value

A data frame with fitted values and cluster assignments, keyed by id. For sfclust_stars objects, a stars object is returned instead.

Examples

# \donttest{
if (requireNamespace("INLA", quietly = TRUE)) {
library(sfclust)

data(stgaus)
result <- sfclust(stgaus, formula = y ~ f(id_time, model = "rw1"), niter = 10,
  nmessage = 1)

# Estimated values ordering clusters by size
df_est <- fitted(result, sort = TRUE)

# Estimated values aggregated by cluster
df_est <- fitted(result, aggregate = TRUE)

# Estimated values using a particular clustering sample
df_est <- fitted(result, sample = 3)
}
#> Iteration 1: clusters = 10, births = 0, deaths = 0, changes = 0, hypers = 0, log_mlike = -674.186842125447
#> Iteration 2: clusters = 11, births = 1, deaths = 0, changes = 0, hypers = 0, log_mlike = -512.007403492225
#> Iteration 3: clusters = 11, births = 1, deaths = 0, changes = 0, hypers = 0, log_mlike = -512.007403492225
#> Iteration 4: clusters = 11, births = 1, deaths = 0, changes = 0, hypers = 0, log_mlike = -512.007403492225
#> Iteration 5: clusters = 11, births = 1, deaths = 0, changes = 0, hypers = 0, log_mlike = -512.007403492225
#> Iteration 6: clusters = 12, births = 2, deaths = 0, changes = 0, hypers = 0, log_mlike = -444.59932393423
#> Iteration 7: clusters = 12, births = 2, deaths = 0, changes = 0, hypers = 0, log_mlike = -444.59932393423
#> Iteration 8: clusters = 12, births = 2, deaths = 0, changes = 0, hypers = 0, log_mlike = -444.59932393423
#> Iteration 9: clusters = 12, births = 2, deaths = 0, changes = 1, hypers = 0, log_mlike = -281.109178282004
#> Iteration 10: clusters = 12, births = 2, deaths = 0, changes = 1, hypers = 0, log_mlike = -281.109178282004
# }