broom (version 0.5.6)

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 cross 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, ...)

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. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Value

A tibble::tibble with one row per component:

component

Cluster id as a factor. For a model k clusters, these will be as.factor(1:k), or as.factor(0:k) if there's a noise term.

size

Number of observations assigned to component

proportion

The mixing proportion of each component

variance

In case of one-dimensional and spherical models, the variance for each component, omitted otherwise. NA for noise component

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

See Also

tidy(), mclust::Mclust()

Other mclust tidiers: augment.Mclust()

Examples

# NOT RUN {
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)
  ) %>% 
  select(-num_points, -cluster) %>%
  tidyr::unnest(x1, x2)

m <- mclust::Mclust(points)

tidy(m)
augment(m, points)
glance(m)

# }