plot the Classical multidimensional scaling of a corx object
plot_mds(corx, k = NULL, abs = TRUE, ...)the corx object, or a matrix of correlation coefficients
a numeric, the number of clusters. If set to "auto" will be equal to the number of principal components that explain more than 5% of total variance.
if TRUE (the default) negative correlations will be turned positive. This means items with high negative correlations will be treated as highly similar.
additional arguments passed to ggpubr::ggscatter
plot_mds performs classic multidimensional scaling on a correlation matrix. The correlation matrix is first converted to a distance matrix using psych::cor2dist. This function employs the following formula: $$dist = \sqrt(2*(1-r))$$ These distances are then passed to stats::cmdscale where k = 2. To compute \(latex\), distances are predict from the cmdscale output and correlated with input distances. This correlation is squared. If the value of \(R^2\) is less than 70 The position of variables is then plotted with ggplot2. Clusters of items are identified using stats::kmeans. The number of clusters is determined using principal component analysis unless specified.
Carlson, D.L., 2017. Quantitative methods in archaeology using R. Cambridge University Press.