`estimate_profiles_mplus.Rd`

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

estimate_profiles_mplus(df, ..., latent_vars = NULL, n_profiles, idvar = NULL, data_filename = "d.dat", script_filename = "i.inp", output_filename = "i.out", savedata_filename = "d-mod.dat", variances = "equal", covariances = "zero", model = NULL, starts = c(100, 10), m_iterations = 500, st_iterations = 20, convergence_criterion = 1e-06, remove_tmp_files = TRUE, print_input_file = FALSE, return_save_data = TRUE, optseed = NULL, n_processors = 1, cluster_ID = NULL, include_VLMR = TRUE, include_BLRT = FALSE, return_all_stats = FALSE)

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

latent_vars | defaults to NULL; specification for the latent varibles as a list, i.e. list(beh = c(1, 2), cog = c(3, 4), aff = (5, 6)), where the integers represent the position of the variables passed to the function and how they correspond to the latent variables, which are named |

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 |

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 |

variances | how the variable variances are estimated; defaults to "equal" (to be constant across profiles); other option is "varying" (to be varying across profiles) |

covariances | how the variable covariances are estimated; defaults to "zero" (to not be estimated, i.e. for the covariance matrix to be diagonal); other options are "varying" (to be varying across profiles) and "equal" (to be constant across profiles) |

model | which model to estimate (DEPRECATED; use variances and covariances instead) |

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 |

cluster_ID | clustering variable (i.e., if data are from students clustered into distinct classrooms) to be used as cluster variables as part of the type = complex option |

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 |

return_all_stats | defaults to FALSE; if TRUE, returns as a one-row data frame all of the statistics returned from compare_solutions_mplus() |

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) # }