Description Usage Arguments Details Value Examples

View source: R/estimate-profiles.R

Estimate parameters for profiles for a specific solution

1 2 3 4 5 |

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

`...` |
unquoted variable names separated by commas |

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

`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) |

`to_return` |
character string for either "tibble" (or "data.frame") or "mclust" if "tibble" is selected, then data with a column for profiles is returned; if "mclust" is selected, then output of class mclust is returned |

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

`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 |

`return_posterior_probs` |
TRUE or FALSE (only applicable if to_return == "tibble"); whether to include posterior probabilities in addition to the posterior profile classification; defaults to TRUE |

`return_orig_df` |
TRUE or FALSE (if TRUE, then the entire data.frame is returned; if FALSE, then only the variables used in the model are returned) |

`prior_control` |
whether to include a regularizing prior; defaults to false |

`print_which_stats` |
if set to "some", prints (as a message) the log-likelihood, BIC, and entropy; if set to "all", prints (as a message) all information criteria and other statistics about the model; if set to any other values, then nothing is printed |

Creates profiles (or estimates of the mixture components) for a specific mclust model in terms of the specific number of mixture components and the structure of the residual covariance matrix

a tibble with the data and profile/class assignment and the posterior probability for that profile; the output of this function can be passed to the plot_profiles() function to create a ggplot2 plot of the profiles. If the argument to_return = "mclust" is added to the function call) an mclust model object, which can be inspected or plotted with mclust functions.

1 2 3 | ```
estimate_profiles(iris,
Sepal.Length, Sepal.Width, Petal.Length, Petal.Width,
n_profiles = 3)
``` |

jrosen48/tidyLPA documentation built on Oct. 13, 2018, 6:54 p.m.

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