tidy.Mclust: Tidy a(n) Mclust object

Description

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.

Usage

# S3 method for Mclust
tidy(x, ...)

Value

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.

Arguments

x

An Mclust object return from mclust::Mclust().

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

Examples

Run this code

if (FALSE) { # rlang::is_installed("mclust")

# load library for models and data
library(mclust)

# load data manipulation libraries
library(dplyr)
library(tibble)
library(purrr)
library(tidyr)

set.seed(27)

centers <- tibble(
  cluster = factor(1:3),
  # number points in each cluster
  num_points = c(100, 150, 50),
  # x1 coordinate of cluster center
  x1 = c(5, 0, -3), 
  # x2 coordinate of cluster center
  x2 = c(-1, 1, -2) 
)

points <- centers %>%
  mutate(
    x1 = map2(num_points, x1, rnorm),
    x2 = map2(num_points, x2, rnorm)
  ) %>%
  select(-num_points, -cluster) %>%
  unnest(c(x1, x2))

# fit model
m <- Mclust(points)

# summarize model fit with tidiers
tidy(m)
augment(m, points)
glance(m)

}

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