ShinyItemAnalysis (version 1.5.4)

plot_corr: Compute and plot an item correlation matrix

Description

Computes and visualizes an item correlation matrix (also known as a heatmap), offering several correlation "types" and optional clustering (with possible cluster outlining). The function relies on ggplot2::ggplot(), providing a high customisability using "the grammar of graphics" (see the examples below).

Usage

plot_corr(
  Data,
  cor = c("polychoric", "tetrachoric", "pearson", "spearman", "none"),
  clust_method = "none",
  n_clust = 0L,
  shape = c("circle", "square"),
  labels = FALSE,
  labels_size = 3,
  line_size = 0.5,
  line_col = "black",
  line_alpha = 1,
  fill = NA,
  fill_alpha = NA,
  ...
)

Value

An object of class ggplot and/or gg.

Arguments

Data

matrix, data.frame or tibble: either a data.frame with scored items (as columns, one observation per row), or a correlation matrix.

cor

character: correlation "type" used to correlation matrix computation; available options are polychoric, tetrachoric, pearson, spearman, or none (in case you provide the correlation matrix as Data).

clust_method

character: optional clustering method, available options are: ward.D, ward.D2, single, complete, average (= UPGMA), mcquitty (= WPGMA), median (= WPGMC), centroid (= UPGMC) or none (clustering disabled). See hclust() for a detailed description of available options.

n_clust

integer: the number of clusters you want to be outlined. When set to zero (the default), no cluster are outlined, but items still do get sorted according to clust_method (if not set to none).

shape

character: tile appearance; either circle (default) to map the correlation coefficient to circle size and color, or square to draw square-shaped tiles with only shade denoting the coefficient magnitude. You can use an unambiguous abbreviation of the two.

labels

logical: when TRUE, the correlation coefficients are plotted onto tiles.

labels_size

numeric: label size in points (pts).

line_size

numeric: cluster outline width.

line_col

character: color of the outline, either a HEX code (e.g. "#123456"), or one of R's standard colors (see the colors()).

line_alpha

numeric 0-1: the opacity of the outline.

fill

character: the color used to fill the outlined clusters.

fill_alpha

numeric 0--1: the opacity of the fill color.

...

Arguments passed on to psych::polychoric

correct

Correction value to use to correct for continuity in the case of zero entry cell for tetrachoric, polychoric, polybi, and mixed.cor. See the examples for the effect of correcting versus not correcting for continuity.

smooth

if TRUE and if the tetrachoric/polychoric matrix is not positive definite, then apply a simple smoothing algorithm using cor.smooth

global

When finding pairwise correlations, should we use the global values of the tau parameter (which is somewhat faster), or the local values (global=FALSE)? The local option is equivalent to the polycor solution, or to doing one correlation at a time. global=TRUE borrows information for one item pair from the other pairs using those item's frequencies. This will make a difference in the presence of lots of missing data. With very small sample sizes with global=FALSE and correct=TRUE, the function will fail (for as yet underdetermined reasons.

weight

A vector of length of the number of observations that specifies the weights to apply to each case. The NULL case is equivalent of weights of 1 for all cases.

std.err

std.err=FALSE does not report the standard errors (faster) deprecated

progress

Show the progress bar (if not doing multicores)

ML

ML=FALSE do a quick two step procedure, ML=TRUE, do longer maximum likelihood --- very slow! Deprecated

delete

Cases with no variance are deleted with a warning before proceeding.

max.cat

The maximum number of categories to bother with for polychoric.

Author

Jan Netik
Institute of Computer Science of the Czech Academy of Sciences

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences

Details

Correlation heatmap displays selected type of correlations between items. The color of tiles indicates how much and in which way the items are correlated -- red color means positive correlation and blue color means negative correlation. Correlation heatmap can be reordered using hierarchical clustering method specified with clust_method argument. When the desired number of clusters (argument n_clust) is not zero and some clustering is demanded, the rectangles outlining the found clusters are drawn.

Examples

# use first 20 columns from HCI dataset (the remainder are not items)
HCI <- HCI[, 1:20]

# use Pearson product-moment correlation coefficient for matrix computation
plot_corr(HCI, cor = "pearson")

if (FALSE) {
# use tetrachoric correlation and reorder the resulting heatmap
# using Ward's method
HCI |> plot_corr(cor = "tetrachoric", clust_method = "ward.D")

# outline 3 Ward's clusters with bold yellow line and add labels
HCI |>
  plot_corr(
    n_clust = 3, clust_method = "ward.D2", line_col = "yellow",
    line_size = 1.5, labels = TRUE
  )

# add title and position the legend below the plot
library(ggplot2)
HCI |>
  plot_corr(n_clust = 3) +
  ggtitle("HCI heatmap") +
  theme(legend.position = "bottom")

# mimic the look of corrplot package
plot_corr(HCI, cor = "polychoric", clust_method = "complete", shape = "square") +
  scale_fill_gradient2(
    limits = c(-.1, 1),
    breaks = seq(-.1, 1, length.out = 12),
    guide = guide_colorbar(
      barheight = .8, barwidth = .0275,
      default.unit = "npc",
      title = NULL, frame.colour = "black", ticks.colour = "black"
    )
  ) +
  theme(axis.text = element_text(colour = "red", size = 12))
}