create_profiles_cluster: Create profiles of observed variables using two-step cluster...

In jrosen48/prcr: Person-Centered Analysis

Description

Create profiles of observed variables using two-step cluster analysis

Usage

create_profiles_cluster(
  df,
  ...,
  n_profiles,
  to_center = FALSE,
  to_scale = FALSE,
  distance_metric = "squared_euclidean",
  linkage = "complete"
)

Arguments

df	with two or more columns with continuous variables
...	unquoted variable names separated by commas
n_profiles	The specified number of profiles to be found for the clustering solution
to_center	Boolean (TRUE or FALSE) for whether to center the raw data with M = 0
to_scale	Boolean (TRUE or FALSE) for whether to scale the raw data with SD = 1
distance_metric	Distance metric to use for hierarchical clustering; "squared_euclidean" is default but more options are available (see ?hclust)
linkage	Linkage method to use for hierarchical clustering; "complete" is default but more options are available (see ?dist)

Details

Function to create a specified number of profiles of observed variables using a two-step (hierarchical and k-means) cluster analysis.

Value

A list containing the prepared data, the output from the hierarchical and k-means cluster analysis, the r-squared value, raw clustered data, processed clustered data of cluster centroids, and a ggplot object.

Examples

d <- pisaUSA15
m3 <- create_profiles_cluster(d, 
                              broad_interest, enjoyment, instrumental_mot, self_efficacy,
                              n_profiles = 3)
summary(m3)

jrosen48/prcr documentation built on Feb. 9, 2020, 5:15 p.m.