tame (version 0.2.0)

summary.medic: Summary of medic object

Description

Make cluster characterizing summaries.

Usage

# S3 method for medic
summary(
  object,
  only = NULL,
  clusters = NULL,
  outputs = "all",
  additional_data = NULL,
  ...
)

Value

A list of clustering characteristics of class summary.medic is returned. It can contain any of the following characteristics:

Cluster Frequencies

The number of individuals assigned to each cluster and the associated frequency of assignment.

Medication Frequencies

The number of individuals with a specific ATC code within a cluster. Moreover, it calculates the percentage of people with this medication assigned to this cluster and the percent of people within the cluster with this medication.

Comedication Count

The number of ATC codes an individual has, and then outputs the number of individuals within a cluster that has that many ATC codes. Moreover, various relevant percentages or calculated. See Value below for more details on these percentages.

Timing Trajectories

The number of unique timing trajectories in each cluster, and the average timing trajectories in each cluster.

Timing and ATC group interactions

The number of people with unique timing trajectory and ATC group, as given by atc_groups, in each cluster.

Arguments

object

An object for which a summary is desired.

only

<data-masking> Expressions that return a logical value, and are defined in terms of the variables in object and/or additional_data.

The default NULL selects all clusterings in object.

clusters

<tidy-select> An unquoted expression naming the cluster or clusters in object one wants to see summaries of. Names can be used as if they were positions in the data frame, so expressions like I:IV can be used to select a range of clusters.

The default NULL selects all clusters in the chosen clusterings of object.

outputs

A character vector naming the desired characteristics to output. The default names all possible output types.

additional_data

A data frame with additional data that may be (left-)joined onto the parameters in object. This is often used in conjuction with only to select specific clusterings based on additional_data.

...

Additional arguments passed to the specific summary sub-function.

Examples

clust <- medic(
  complications,
  id = id,
  atc = atc,
  k = 3:5,
  timing = first_trimester:third_trimester
)
summary(clust)