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.
# S3 method for Mclust tidy(x, ...)
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
tibble::tibble() with columns:
The mixing proportion of each component
Number of points assigned to cluster.
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
In case of one-dimensional and spherical models, the variance for each component, omitted otherwise. NA for noise component
Cluster id as a factor.
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)#> Error in select(., -num_points, -cluster): unused arguments (-num_points, -cluster)m <- mclust::Mclust(points)#> Error in as.vector(x, mode): cannot coerce type 'closure' to vector of type 'any'tidy(m)#> Error in tidy(m): object 'm' not foundaugment(m, points)#> Error in augment(m, points): object 'm' not foundglance(m)#> Error in glance(m): object 'm' not found