plot.emMSmix: Plot the MLEs for the fitted mixture of Mallows models with Spearman distance

Description

plot method for class "emMSmix".

Usage

# S3 method for emMSmix
plot(x, max_scale_w = 20, mar_lr = 0.4, mar_tb = 0.2, ...)

Value

A list of 2 labelled plots, namely: i) bump_plot: a bump plot comparing the component-specific consensus rankings of the fitted mixture of Mallows models with Spearman distance (the size of the dots of each consensus ranking is proportional to the weight of the corresponding component); and ii) est_clust_prob: a heatmap of the estimated component membership probabilities is returned when n_clust > 1, otherwise NULL.

Arguments

x: An object of class "emMSmix" returned by fitMSmix.
max_scale_w: Positive scalar: maximum magnification of the dots in the bump plot, set proportional to the MLEs of the weights. Defaults to 20.
mar_lr: Numeric: margin for the left and right side of the plot. Defaults to 0.4.
mar_tb: Numeric: margin for the bottom and top side of the plot. Defaults to 0.2.
...: Further arguments passed to or from other methods (not used).

References

Sjoberg D (2020). ggbump: Bump Chart and Sigmoid Curves. R package version 0.1.10. https://CRAN.R-project.org/package=ggbump.

Wickham H et al. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, DOI: 10.21105/joss.01686.

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.

Examples

Run this code

## Example 1. Fit and plot a 3-component mixture of Mallows models with Spearman distance
## to the Antifragility dataset.
r_antifrag <- ranks_antifragility[, 1:7]
set.seed(123)
mms_fit <- fitMSmix(rankings = r_antifrag, n_clust = 3, n_start = 10)
p_mms_fit <- plot(mms_fit)
p_mms_fit$bump_plot()
p_mms_fit$est_clust_prob()