Estimate parameters for profiles for a specific solution

estimate_profiles(df, ..., n_profiles, model = 1, to_return = "tibble",
center_raw_data = FALSE, scale_raw_data = FALSE,
return_posterior_probs = TRUE, return_orig_df = FALSE,
prior_control = FALSE, print_which_stats = "some")

## Arguments

df data.frame with two or more columns with continuous variables unquoted variable names separated by commas the number of profiles (or mixture components) to be estimated 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 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 logical for whether to center (M = 1) the raw data (before clustering); defaults to FALSE logical for whether to scale (SD = 1) the raw data (before clustering); defaults to FALSE TRUE or FALSE (only applicable if to_return == "tibble"); whether to include posterior probabilities in addition to the posterior profile classification; defaults to TRUE TRUE or FALSE (if TRUE, then the entire data.frame is returned; if FALSE, then only the variables used in the model are returned) whether to include a regularizing prior; defaults to false 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

## Value

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

## Details

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

## Examples

estimate_profiles(iris,
Sepal.Length, Sepal.Width, Petal.Length, Petal.Width,
model = 1,
n_profiles = 3)#> Fit varying means, equal variances, covariances fixed to 0 (Model 1) model with 3 profiles.#> LogLik is 361.429#> BIC is 813.05#> Entropy is 0.979#> # A tibble: 150 x 6
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width profile posterior_prob
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>            <dbl>
#>  1         5.10        3.50         1.40       0.200 1                   1.
#>  2         4.90        3.00         1.40       0.200 1                   1.
#>  3         4.70        3.20         1.30       0.200 1                   1.
#>  4         4.60        3.10         1.50       0.200 1                   1.
#>  5         5.00        3.60         1.40       0.200 1                   1.
#>  6         5.40        3.90         1.70       0.400 1                   1.
#>  7         4.60        3.40         1.40       0.300 1                   1.
#>  8         5.00        3.40         1.50       0.200 1                   1.
#>  9         4.40        2.90         1.40       0.200 1                   1.
#> 10         4.90        3.10         1.50       0.100 1                   1.
#> # ... with 140 more rows