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 class 'Mclust'
tidy(x, ...)
Arguments
- x
An
Mclust
object return frommclust::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 passconf.lvel = 0.9
, all computation will proceed usingconf.level = 0.95
. Two exceptions here are:
See also
Other mclust tidiers:
augment.Mclust()
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.
Examples
# 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))
#> Error in select(mutate(centers, x1 = map2(num_points, x1, rnorm), x2 = map2(num_points, x2, rnorm)), -num_points, -cluster): unused arguments (-num_points, -cluster)
# fit model
m <- Mclust(points)
#> Error in as.vector(x, mode): cannot coerce type 'closure' to vector of type 'any'
# summarize model fit with tidiers
tidy(m)
#> Error: object 'm' not found
augment(m, points)
#> Error: object 'm' not found
glance(m)
#> Error: object 'm' not found