This functions extracts the cluster assignments from an lcc object. One can specify which for which number of clusters the assignments should be returned.

## Usage

get_clusters(cluster_solution, number_clusters = NULL)

## Arguments

cluster_solution

an lcc object

number_clusters

default is NULL to return all assignments. Otherwise specify a numeric vector with the number of clusters for which the assignments should be returned, e.g. 2:4

## Value

a data.frame with an ID column (the name of the ID column was specified by the user when calling the longitudinal_consensus_cluster) function and one column with cluster assignments for every specified number of clusters. Only the assignments included in number_clusters are returned in the form of columns with the names assignment_num_clus_x

## Examples

# not run
set.seed(5)
test_data <- data.frame(patient_id = rep(1:10, each = 4),
visit = rep(1:4, 10),
var_1 = c(rnorm(20, -1), rnorm(20, 3)) +
rep(seq(from = 0, to = 1.5, length.out = 4), 10),
var_2 = c(rnorm(20, 0.5, 1.5), rnorm(20, -2, 0.3)) +
rep(seq(from = 1.5, to = 0, length.out = 4), 10))
model_list <- list(flexmix::FLXMRmgcv(as.formula("var_1 ~ .")),
flexmix::FLXMRmgcv(as.formula("var_2 ~ .")))
clustering <- longitudinal_consensus_cluster(
data = test_data,
id_column = "patient_id",
max_k = 2,
reps = 3,
model_list = model_list,
flexmix_formula = as.formula("~s(visit, k = 4) | patient_id"))
#> 2 : *
#> 2 : *
#> 2 : *
cluster_assignments <- get_clusters(clustering, number_clusters = 2)
# end not run