`compare_solutions.Rd`

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)

df | data.frame with two or more columns with continuous variables |
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... | 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 |

a ggplot2 plot of the BIC values for the explored models

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)

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