Description Usage Arguments Details Value Examples
View source: R/create_profiles_cluster.R
Create profiles of observed variables using two-step cluster analysis
1 2 3 4 5 6 7 8 9 | create_profiles_cluster(
df,
...,
n_profiles,
to_center = FALSE,
to_scale = FALSE,
distance_metric = "squared_euclidean",
linkage = "complete"
)
|
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) |
Function to create a specified number of profiles of observed variables using a two-step (hierarchical and k-means) cluster analysis.
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.
1 2 3 4 5 | d <- pisaUSA15
m3 <- create_profiles_cluster(d,
broad_interest, enjoyment, instrumental_mot, self_efficacy,
n_profiles = 3)
summary(m3)
|
Prepared data: Removed 354 incomplete cases
Hierarchical clustering carried out on: 5358 cases
K-means algorithm converged: 5 iterations
Clustered data: Using a 3 cluster solution
Calculated statistics: R-squared = 0.424
broad_interest enjoyment instrumental_mot self_efficacy
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2.200 1st Qu.:2.400 1st Qu.:1.500 1st Qu.:1.625
Median :2.800 Median :3.000 Median :2.000 Median :2.000
Mean :2.655 Mean :2.782 Mean :2.072 Mean :2.134
3rd Qu.:3.200 3rd Qu.:3.000 3rd Qu.:2.500 3rd Qu.:2.500
Max. :5.000 Max. :4.000 Max. :4.000 Max. :4.000
cluster
Min. :1.000
1st Qu.:1.000
Median :2.000
Mean :1.784
3rd Qu.:2.000
Max. :3.000
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