tidySEM (version 0.2.10)

plot_bivariate: Create correlation plots for a mixture model

Description

Creates a faceted plot of two-dimensional correlation plots and unidimensional density plots for a single mixture model.

Usage

plot_bivariate(
  x,
  variables = NULL,
  sd = TRUE,
  cors = TRUE,
  rawdata = TRUE,
  bw = FALSE,
  alpha_range = c(0, 0.1),
  return_list = FALSE,
  ...
)

Value

An object of class 'ggplot'.

Arguments

x

An object for which a method exists.

variables

Which variables to plot. If NULL, plots all variables that are present in the model.

sd

Logical. Whether to show the estimated standard deviations as lines emanating from the cluster centroid.

cors

Logical. Whether to show the estimated correlation (standardized covariance) as ellipses surrounding the cluster centroid.

rawdata

Logical. Whether to plot raw data, weighted by posterior class probability.

bw

Logical. Whether to make a black and white plot (for print) or a color plot. Defaults to FALSE, because these density plots are hard to read in black and white.

alpha_range

Numeric vector (0-1). Sets the transparency of geom_density and geom_point.

return_list

Logical. Whether to return a list of ggplot objects, or just the final plot. Defaults to FALSE.

...

Additional arguments.

Author

Caspar J. van Lissa

Examples

if(requireNamespace("OpenMx", quietly = TRUE)){
library(OpenMx)
iris_sample <- iris[c(1:5, 145:150), c("Sepal.Length", "Sepal.Width")]
names(iris_sample) <- c("x", "y")
res <- mx_profiles(iris_sample, classes = 2)
plot_bivariate(res, rawdata = FALSE)
}