Create a heatmap of metric values by subject based on hierarchical clustering order

Create a heatmap of metric values by subject based on hierarchical clustering order

Usage

metrics_heatmap(
  data = NULL,
  metrics = NULL,
  metric_cluster = 6,
  clustering_method = "complete",
  clustering_distance_metrics = "correlation",
  clustering_distance_subjects = "correlation",
  tz = ""
)

Arguments

data

DataFrame object with column names "id", "time", and "gl".

metrics

precalculated metric values, with first column corresponding to subject id. If 'NULL', the metrics are calculated from supplied 'data' using all_metrics

metric_cluster

number of visual metric clusters, default value is 6

clustering_method

the agglomeration method for hierarchical clustering, accepts same values as hclust, default value is 'complete'

clustering_distance_metrics

the distance measure for metrics clustering, accepts same values as dist, default value is 'correlation' distance

clustering_distance_subjects

the distance measure for subjects clustering, accepts same values as dist, default value is 'correlation' distance

tz

Default: "". A character string specifying the time zone to be used. System-specific (see as.POSIXct), but " " is the current time zone, and "GMT" is UTC (Universal Time, Coordinated). Invalid values are most commonly treated as UTC, on some platforms with a warning.

Value

A heatmap of metrics by subjects generated via pheatmap

Examples

# Using pre-calculated sd metrics only rather than default (all metrics)
mecs = sd_measures(example_data_5_subject)
metrics_heatmap(metrics = mecs)