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Pairs-plot for Interval-valued Symbolic data.

Source: R/plots.r
plot_pairs_int.Rd

Adapted from pairs.panels (R package "psych") shows a scatter plot of matrices, with bivariate symbolic scatter plots below the diagonal, variables' names on the diagonal, and all the symbolic correlations above the diagonal. Useful for descriptive statistics of symbolic objects described by interval variables.

Usage

plot_pairs_int(
  data,
  type = c("rectangles", "crosses", "crosses2"),
  cex.cor = 2,
  corr = NULL,
  palette = rainbow(nrow(data)),
  fill_col = "gray50",
  is_outlier = NULL,
  ...
)

Arguments

data

An intData object containing the macrodata/interval data

type

The type of plot to generate: "rectangles" or "crosses" or "crosses2". Default is "rectangles".

cex.cor

Character expansion factor

corr

A matrix with the symbolic correlations; if not provided the upper panel is omitted

palette

A vector with colors for each observation.

fill_col

If type="rectangles", a vector with colors for the fill of each observation, or a single color for all observations. Default is "gray50".

is_outlier

A vector with logical values indicating if the observation is an outlier or not. It makes the line width of the outlying observations thicker. Default is NULL.

...

Additional graphical parameters.

Value

A scatter plot matrix is drawn in the graphic window. The lower off diagonal draws scatter plots, the diagonal variables' names, the upper off diagonal reports all the symbolic correlations.

Examples

data(creditcard)
credit_card_int <- creditcard$intData

# Compute covariance and correlation matrices
credit_card_cov <- int_cov(credit_card_int)
credit_card_cor <- cov2cor(credit_card_cov)
plot_pairs_int(credit_card_int,
                  corr = credit_card_cor,
                  labels = colnames(credit_card_int))


# Alternatively, highlight outliers in the scatter plot and use the robust correlation matrix
# Obtain reweighted IMCD estimates using farness cutoff
credit_card_IMCD <- IMCD(credit_card_int, 
                         m = floor(nrow(credit_card_int)*0.75), 
                         cutoff = "farness", 
                         cutoff_lvl = 0.9)

# Detect outliers using farness cutoff
credit_card_outliers <- int_outliers(credit_card_IMCD$robust_dist, 
                                     cutoff = "farness", 
                                     cutoff_lvl = 0.9)

outliers_colors <- rep('gray50',credit_card_int@NObs)
names(outliers_colors) <- rownames(credit_card_int)
outliers_colors[credit_card_outliers$outliers_names] = 'red'

plot_pairs_int(credit_card_int, 
                  corr = cov2cor(credit_card_IMCD$cov_IMCD), 
                  palette = outliers_colors,
                  labels = colnames(credit_card_int),
                  type = "rectangles",
                  is_outlier = credit_card_outliers$is_outlier)