Estimate parameters for profiles for a specific solution (requires purchasing and installing MPlus to use)

estimate_profiles_mplus(df, ..., n_profiles, idvar = NULL, the_title = "test", data_filename = "d.dat", script_filename = "i.inp", output_filename = "i.out", savedata_filename = "d-mod.dat", model = 1, starts = c(20, 4), m_iterations = 500, st_iterations = 10, convergence_criterion = 1e-06, remove_tmp_files = TRUE, print_input_file = FALSE, return_save_data = TRUE, optseed = NULL, n_processors = 1, include_VLMR = TRUE, include_BLRT = FALSE)

df | data.frame with two or more columns with continuous variables |
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... | unquoted variable names separated by commas |

n_profiles | the number of profiles (or mixture components) to be estimated |

idvar | optional name of the column to be used as the ID variable (should be supplied as a string). Defaults to |

the_title | title of the model; defaults to test |

data_filename | name of data file to prepare; defaults to d.dat |

script_filename | name of script to prepare; defaults to i.inp |

output_filename | name of the output; defaults to o.out |

savedata_filename | name of the output for the save data (with the original data conditional probabilities); defaults to o-mod.out |

model | the mclust model to explore: 1 (varying means, equal variances, and residual covariances fixed to 0); 2 (varying means, equal variances and covariances; 3 (varying means and variances, covariances fixed to 0), 4 (varying means and covariances, equal variances; can only be specified in Mplus); 5 (varying means, equal variances, varying covariances); and 6 (varying means, variances, and covariances), in order least to most freely-estimated; see the introductory vignette for more information |

starts | number of initial stage starts and number of final stage optimizations; defaults to c(20, 4); can be set to be more conservative to c(500, 50) |

m_iterations | number of iterations for the EM algorithm; defaults to 500 |

st_iterations | the number of initial stage iterations; defaults to 10; can be set more to be more conservative to 50 |

convergence_criterion | convergence criterion for the Quasi-Newton algorithm for continuous outcomes; defaults to 1E-6 (.000001); can be set more conservatively to 1E-7 (.0000001) |

remove_tmp_files | whether to remove data, script, and output files; defaults to TRUE |

print_input_file | whether to print the input file to the console |

return_save_data | whether to return the save data (with the original data and the posterior probabilities for the classes and the class assignment) as a data.frame along with the MPlus output; defaults to TRUE |

optseed | random seed for analysis |

n_processors | = 1 |

include_VLMR | whether to include the Vu-Lo-Mendell-Rubin likelihood-ratio test; defaults to TRUE |

include_BLRT | whether to include the bootstrapped LRT; defaults to FALSE because of the time this takes to run |

either a tibble or a ggplot2 plot of the BIC values for the explored models

Creates an mplus model (.inp) and associated data file (.dat)

not_run({ m <- estimate_profiles_mplus(iris, Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, n_profiles = 2, model = 1) })