Creates a faceted plot of two-dimensional correlation plots and unidimensional density plots for an object of class 'tidyProfile'.
plot_bivariate( x, variables = NULL, sd = TRUE, cors = TRUE, rawdata = TRUE, bw = FALSE, alpha_range = c(0, 0.1), return_list = FALSE )
| x | tidyProfile object to plot. A tidyProfile is one element of a tidyLPA analysis. |
|---|---|
| variables | Which variables to plot. If NULL, plots all variables that are present in all models. |
| 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. |
An object of class 'ggplot'.
Caspar J. van Lissa
# Example 1 iris_sample <- iris[c(1:10, 51:60, 101:110), ] # to make example run more quickly if (FALSE) { iris_sample %>% subset(select = c("Sepal.Length", "Sepal.Width")) %>% estimate_profiles(n_profiles = 2, models = 1) %>% plot_bivariate() } # Example 2 if (FALSE) { mtcars %>% subset(select = c("wt", "qsec", "drat")) %>% poms() %>% estimate_profiles(3) %>% plot_bivariate() }