Explore BIC for various models and numbers of profiles

compare_solutions(df, ..., n_profiles_range = 1:9,
  models = list(c("equal", "zero"), c("varying", "zero"), c("equal",
  "equal"), c("varying", "varying")), center_raw_data = FALSE,
  scale_raw_data = FALSE, statistic = "BIC", return_table = FALSE,
  prior_control = F)

Arguments

df

data.frame with two or more columns with continuous variables

...

unquoted variable names separated by commas

n_profiles_range

a vector with the range of the number of mixture components to explore; defaults to 1 through 9 (1:9)

models

which models to include as a list of vectors; for each vector, the first value represents how the variances are estimated and the second value represents how the covariances are estimated; defaults to list(c("equal", "zero"), c("varying", "zero"), c("equal", "equal"), c("varying", "varying"))

center_raw_data

logical for whether to center (M = 1) the raw data (before clustering); defaults to FALSE

scale_raw_data

logical for whether to scale (SD = 1) the raw data (before clustering); defaults to FALSE

statistic

what statistic to plot; BIC or ICL are presently available as options

return_table

logical (TRUE or FALSE) for whether to return a table of the output instead of a plot; defaults to FALSE

prior_control

whether to include a regularizing prior; defaults to false

Value

a ggplot2 plot of the BIC values for the explored models

Details

Explore the BIC values of a range of models in terms of a) the structure of the residual covariance matrix and b) the number of mixture components (or profiles)

Examples

compare_solutions(iris, Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)