Create latent profile plots — plot

Creates a profile plot (ribbon plot) according to best practices, focusing on the visualization of classification uncertainty by showing:

Bars reflecting a confidence interval for the class centroids
Boxes reflecting the standard deviations within each class; a box encompasses +/- 64 percent of the observations in a normal distribution
Raw data, whose transparency is weighted by the posterior class probability, such that each observation is most clearly visible for the class it is most likely to be a member of.

plot_profiles(
  x,
  variables = NULL,
  ci = 0.95,
  sd = TRUE,
  add_line = FALSE,
  rawdata = TRUE,
  bw = FALSE,
  alpha_range = c(0, 0.1),
  ...
)

# S3 method for default
plot_profiles(
  x,
  variables = NULL,
  ci = 0.95,
  sd = TRUE,
  add_line = FALSE,
  rawdata = TRUE,
  bw = FALSE,
  alpha_range = c(0, 0.1),
  ...
)

Arguments

x: An object containing the results of a mixture model analysis.
variables: A character vectors with the names of the variables to be plotted (optional).
ci: Numeric. What confidence interval should the error bars span? Defaults to a 95 percent confidence interval. Set to NULL to remove error bars.
sd: Logical. Whether to display a box encompassing +/- 1SD Defaults to TRUE.
add_line: Logical. Whether to display a line, connecting cluster centroids belonging to the same latent class. Defaults to FALSE, as it is not recommended to imply connectivity between the different variables on the X-axis.
rawdata: Should raw data be plotted in the background? Setting this to TRUE might result in long plotting times.
bw: Logical. Should the plot be black and white (for print), or color?
alpha_range: The minimum and maximum values of alpha (transparency) for the raw data. Minimum should be 0; lower maximum values of alpha can help reduce overplotting.
...: Arguments passed to and from other functions.

Value

An object of class 'ggplot'.

Author

Caspar J. van Lissa

Examples

df_plot <- data.frame(Variable = "x1",
Class = "class1",
Classes = 1,
Model = "equal var 1",
Value = 3.48571428571429,
se = 0.426092805342181,
Value.Variances = 3.81265306156537,
se.Variances = 1.17660769119959)
plot_profiles(list(df_plot = df_plot, df_raw = NULL),
ci = NULL, sd = FALSE, add_line = FALSE,
rawdata = FALSE, bw = FALSE)