Plot variable means and variances by profile

plot_profiles(x, to_center = FALSE, to_scale = FALSE,
plot_what = "tibble", plot_error_bars = TRUE, plot_rawdata = TRUE,
ci = 0.95)

## Arguments

x output from estimate_profiles() whether to center the data before plotting whether to scale the data before plotting whether to plot tibble or mclust output from estimate_profiles(); defaults to tibble whether to plot error bars (representing the 95 percent confidence interval for the mean of each variable) whether to plot raw data; defaults to TRUE confidence interval to plot (defaults to 0.95)

## Details

Plot the variable means and variances for data frame output from estimate_profiles()

Plot the variable means and variances for data frame output from estimate_profiles(). When plot_what is set to 'mclust', the errorbars represent non-parametric confidence intervals, obtained using bootstrapping (100 samples). Note that 100 samples might be adequate for plotting, but is low for inference. If the number of participants per class is highly unbalanced, then weighted likelihood bootstrapping is used to ensure that each case is represented in the bootstrap samples (see O'Hagan, Murphy, Scrucca, and Gormley, 2015).

## Examples

m3 <- estimate_profiles(iris,
Sepal.Length, Sepal.Width, Petal.Length, Petal.Width,
n_profiles = 3)#> Fit Equal variances and covariances fixed to 0 (model 1) model with 3 profiles.#> LogLik is 361.429#> BIC is 813.05#> Entropy is 0.979plot_profiles(m3)
m3 <- estimate_profiles(iris,
Sepal.Length, Sepal.Width, Petal.Length, Petal.Width,
n_profiles = 3, to_return = "mclust")#> Fit Equal variances and covariances fixed to 0 (model 1) model with 3 profiles.#> LogLik is 361.429#> BIC is 813.05#> Entropy is 0.979plot_profiles(m3, plot_what = "mclust")