opticskxi (version 1.2.0)

opticskxi_pipeline: OPTICS k-Xi models comparison pipeline

Description

Computes OPTICS k-Xi models based on a parameter grid, binds results in a data frame, and computes distance based metrics for each model.

Usage

opticskxi_pipeline(
  m_data,
  df_params = expand.grid(n_xi = 1:10, pts = c(20, 30, 40), dist = c("euclidean",
    "abscorrelation"), dim_red = c("identity", "PCA", "ICA"), n_dimred_comp = c(5, 10,
    20)),
  metrics_dist = c("euclidean", "cosine"),
  max_size_ratio = 1,
  n_min_clusters = 0,
  n_cores = 1,
  ...
)

Value

Input parameter data frame with with results binded in columns optics, clusters and metrics.

Arguments

m_data

Data matrix

df_params

Parameter grid for the OPTICS k-Xi function call and optional dimension reduction. Required columns: n_xi, pts, dist. Optonal columns: dim_red, n_dim_red.

metrics_dist

Distance used for metrics, either euclidean or cosine.

max_size_ratio

Maximum size ratio of clusters. E.g. for 0.8, if a cluster is larger than 80% of points it will be removed.

n_min_clusters

Minimum number of clusters. Ignored if 0.

n_cores

Number of cores

...

Passed to get_kxi_metrics

See Also

get_best_kxi, ggplot_kxi_metrics, gtable_kxi_profiles

Examples

data('hla')
m_hla <- hla[-c(1:2)] %>% scale

df_params_hla <- expand.grid(n_xi = 3:5, pts = c(20, 30),
  dist = c('manhattan', 'euclidean'))

df_kxi_hla <- opticskxi_pipeline(m_hla, df_params_hla)

ggplot_kxi_metrics(df_kxi_hla, n = 8)
gtable_kxi_profiles(df_kxi_hla) %>% plot

best_kxi_hla <- get_best_kxi(df_kxi_hla, rank = 2)
clusters_hla <- best_kxi_hla$clusters

fortify_pca(m_hla, sup_vars = data.frame(Clusters = clusters_hla)) %>%
  ggpairs('Clusters', ellipses = TRUE, variables = TRUE)