Tidy a(n) Mclust object
Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
## S3 method for class 'Mclust' tidy(x, ...)
x |
An |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble() with columns:
proportion |
The mixing proportion of each component |
size |
Number of points assigned to cluster. |
mean |
The mean for each component. In case of 2+ dimensional models, a column with the mean is added for each dimension. NA for noise component |
variance |
In case of one-dimensional and spherical models, the variance for each component, omitted otherwise. NA for noise component |
component |
Cluster id as a factor. |
Other mclust tidiers:
augment.Mclust()
if (requireNamespace("mclust", quietly = TRUE)) {
library(dplyr)
library(mclust)
set.seed(27)
centers <- tibble::tibble(
cluster = factor(1:3),
num_points = c(100, 150, 50), # number points in each cluster
x1 = c(5, 0, -3), # x1 coordinate of cluster center
x2 = c(-1, 1, -2) # x2 coordinate of cluster center
)
points <- centers %>%
mutate(
x1 = purrr::map2(num_points, x1, rnorm),
x2 = purrr::map2(num_points, x2, rnorm)
) %>%
dplyr::select(-num_points, -cluster) %>%
tidyr::unnest(c(x1, x2))
m <- mclust::Mclust(points)
tidy(m)
augment(m, points)
glance(m)
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