Glance accepts a model object and returns a
with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.
Glance never returns information from the original call to the modelling function. This includes the name of the modelling function or any arguments passed to the modelling function.
Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as
# S3 method for Mclust glance(x, ...)
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
tibble::tibble() with exactly one row and columns:
Bayesian Information Criterion for the model.
Degrees of freedom used by the model.
The log-likelihood of the model. [stats::logLik()] may be a useful reference.
Needs custom info.
A string denoting the model type with optimal BIC
Number mixture components in optimal model
If the other model contains a noise component, the value of the hypervolume parameter. Otherwise `NA`.
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