Assign meta cluster labels to rows of a solutions data frame or extended solutions data frame
Source:R/label_meta_clusters.R
label_meta_clusters.RdGiven a solutions data frame or extended solutions data frame class object and a numeric vector indicating which rows correspond to which meta clusters, assigns meta clustering information to the "meta_clusters" attribute of the data frame.
Arguments
- sol_df
A solutions data frame or extended solutions data frame to assign meta clusters to.
- split_vector
A numeric vector indicating which rows of sol_df should be the split points for meta cluster labeling.
- order
An optional numeric vector indicating how the solutions data frame should be reordered prior to meta cluster labeling. This vector can be obtained by running
get_matrix_order()on an ARI matrix, which itself can be obtained by callingcalc_aris()on a solutions data frame.
Examples
# \donttest{
dl <- data_list(
list(cort_sa, "cortical_surface_area", "neuroimaging", "continuous"),
list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
list(income, "household_income", "demographics", "continuous"),
list(pubertal, "pubertal_status", "demographics", "continuous"),
uid = "unique_id"
)
#> ℹ 175 observations dropped due to incomplete data.
set.seed(42)
my_sc <- snf_config(
dl = dl,
n_solutions = 20,
min_k = 20,
max_k = 50
)
#> ℹ No distance functions specified. Using defaults.
#> ℹ No clustering functions specified. Using defaults.
sol_df <- batch_snf(dl, my_sc)
sol_df
#> 20 cluster solutions of 100 observations:
#> solution nclust mc uid_NDAR_INV0567T2Y9 uid_NDAR_INV0IZ157F8
#> 8;5;250m 1 1 8 NA 5 2
#> 8;5;250m 2 2 4 NA 1 3
#> 8;5;250m 3 3 2 NA 2 1
#> 8;5;250m 4 4 2 NA 2 1
#> 8;5;250m 5 5 3 NA 3 1
#> 8;5;250m 6 6 2 NA 2 1
#> 8;5;250m 7 7 4 NA 1 2
#> 8;5;250m 8 8 6 NA 5 4
#> 8;5;250m 9 9 2 NA 1 2
#> 8;5;250m 0 10 2 NA 2 1
#> 8;5;246m ℹ 10 or ws
#> 8;5;246m ℹ 98 or riables: uid_NDAR_INV0J4PYA5F <dbl>, uid_NDAR_INV10OMKVLE <dbl>,
#> 8;5;246m uid ND NV15FPCW4O <dbl>, uid_NDAR_INV19NB4RJK <dbl>,
#> 8;5;246m uid ND NV1HLGR738 <dbl>, uid_NDAR_INV1KR0EZFU <dbl>,
#> 8;5;246m uid ND NV1L3Y9EOP <dbl>, uid_NDAR_INV1TCP5GNM <dbl>,
#> 8;5;246m uid ND NV1ZHRDJ6B <dbl>, uid_NDAR_INV2EJ41YSZ <dbl>,
#> 8;5;246m uid ND NV2PK6C85M <dbl>, uid_NDAR_INV2XO1PHCT <dbl>, …
#> 3 solutions and 98 observations not shown.
#> Use `print(n = ...)` to change the number of rows printed.
#> Use `t()` to view compact cluster solution format.
#>
sol_aris <- calc_aris(sol_df)
meta_cluster_order <- get_matrix_order(sol_aris)
# `split_vec` found by iteratively plotting ari_hm or by ?shiny_annotator()
split_vec <- c(6, 10, 16)
ari_hm <- meta_cluster_heatmap(
sol_aris,
order = meta_cluster_order,
split_vector = split_vec
)
#> Sorting by order.
mc_sol_df <- label_meta_clusters(
sol_df,
order = meta_cluster_order,
split_vector = split_vec
)
mc_sol_df
#> 20 cluster solutions of 100 observations:
#> solution nclust mc uid_NDAR_INV0567T2Y9 uid_NDAR_INV0IZ157F8
#> 8;5;250m 1 1 8 C 5 2
#> 8;5;250m 2 2 4 C 1 3
#> 8;5;250m 3 3 2 A 2 1
#> 8;5;250m 4 4 2 A 2 1
#> 8;5;250m 5 5 3 B 3 1
#> 8;5;250m 6 6 2 A 2 1
#> 8;5;250m 7 7 4 D 1 2
#> 8;5;250m 8 8 6 C 5 4
#> 8;5;250m 9 9 2 B 1 2
#> 8;5;250m 0 10 2 A 2 1
#> 8;5;246m ℹ 10 or ws
#> 8;5;246m ℹ 98 or riables: uid_NDAR_INV0J4PYA5F <dbl>, uid_NDAR_INV10OMKVLE <dbl>,
#> 8;5;246m uid ND NV15FPCW4O <dbl>, uid_NDAR_INV19NB4RJK <dbl>,
#> 8;5;246m uid ND NV1HLGR738 <dbl>, uid_NDAR_INV1KR0EZFU <dbl>,
#> 8;5;246m uid ND NV1L3Y9EOP <dbl>, uid_NDAR_INV1TCP5GNM <dbl>,
#> 8;5;246m uid ND NV1ZHRDJ6B <dbl>, uid_NDAR_INV2EJ41YSZ <dbl>,
#> 8;5;246m uid ND NV2PK6C85M <dbl>, uid_NDAR_INV2XO1PHCT <dbl>, …
#> 3 solutions and 98 observations not shown.
#> Use `print(n = ...)` to change the number of rows printed.
#> Use `t()` to view compact cluster solution format.
#>
# }