Visualize Variables Factor Map

Plots column/variable principal coordinates.

Usage

viz_variables(x, ...)

viz_columns(x, ...)

# S4 method for class 'MultivariateAnalysis'
viz_columns(
  x,
  ...,
  axes = c(1, 2),
  active = TRUE,
  sup = TRUE,
  labels = FALSE,
  highlight = NULL,
  color = NULL,
  symbol = 16,
  size = c(1, 3),
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  panel.first = NULL,
  panel.last = NULL,
  legend = list(x = "topleft")
)

# S4 method for class 'MultivariateBootstrap'
viz_columns(x, ..., axes = c(1, 2))

# S4 method for class 'PCA'
viz_variables(
  x,
  ...,
  axes = c(1, 2),
  active = TRUE,
  sup = TRUE,
  labels = list(how = "contribution", n = 10),
  highlight = NULL,
  color = NULL,
  symbol = NULL,
  size = 1,
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  panel.first = NULL,
  panel.last = NULL,
  legend = list(x = "topleft")
)

# S4 method for class 'CA'
viz_variables(
  x,
  ...,
  axes = c(1, 2),
  active = TRUE,
  sup = TRUE,
  labels = FALSE,
  highlight = NULL,
  color = NULL,
  symbol = 16,
  size = c(1, 3),
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  panel.first = NULL,
  panel.last = NULL,
  legend = list(x = "topleft")
)

# S4 method for class 'BootstrapPCA'
viz_variables(x, ..., axes = c(1, 2))

Arguments

x: A CA, MCA or PCA object.
...: Currently not used.
axes: A length-two numeric vector giving the dimensions to be plotted.
active: A logical scalar: should the active observations be plotted?
sup: A logical scalar: should the supplementary observations be plotted?
labels: A logical scalar: should labels be drawn? Labeling a large number of points can be computationally expensive and make the graph difficult to read. A selection of points to label can be provided using a list of two named elements, how (a string specifying how to select the labels to be drawn) and n (an integer specifying the number of labels to be drawx). See examples below.
highlight: A vector specifying the information to be highlighted. If NULL (the default), no highlighting is applied. If a single character string is passed, it must be the name of a categorical variable, or one of "observation", "mass", "sum", "contribution" or "cos2" (see augment()).
color: The colors for lines and points (will be mapped to highlight).
symbol: A vector of plotting characters or symbols.
size: A length-two numeric vector giving range of possible sizes (greater than 0; (will be mapped to highlight).
xlim: A length-two numeric vector giving the x limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.
ylim: A length-two numeric vector giving the y limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.
main: A character string giving a main title for the plot.
sub: A character string giving a subtitle for the plot.
panel.first: An expression to be evaluated after the plot axes are set up but before any plotting takes place. This can be useful for drawing background grids.
panel.last: An expression to be evaluated after plotting has taken place but before the axes, title and box are added.
legend: A list of additional arguments to be passed to graphics::legend(); names of the list are used as argument names. If NULL, no legend is displayed.

Value

viz_*() is called for its side-effects: it results in a graphic being displayed. Invisibly returns x.

Note

Be careful: graphical parameters are silently recycled.

Author

N. Frerebeau

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE)
#> 1 qualitative variable was removed: Species.

## Plot individuals
viz_individuals(X, panel.last = graphics::grid())


## Labels of the 10 individuals with highest cos2
viz_individuals(X, labels = list(how = "cos2", n = 10))


## Plot variables
viz_variables(X, panel.last = graphics::grid())


## Graphical parameters
## Continuous values
viz_individuals(X, highlight = iris$Petal.Length, symbol = 16, size = c(1, 2))

viz_individuals(X, highlight = iris$Petal.Length, symbol = 16, size = c(1, 2),
                color = grDevices::hcl.colors(12, "RdPu"))


viz_variables(X, highlight = "contribution",
              color = grDevices::hcl.colors(12, "BluGrn", rev = TRUE),
              size = c(0, 1))


## Discrete values
viz_individuals(X, highlight = iris$Species, symbol = 21:23)

viz_individuals(X, highlight = iris$Species, symbol = 21:23,
                fill = c("#004488", "#DDAA33", "#BB5566"),
                color = "black")


viz_variables(X, highlight = c("Petal", "Petal", "Sepal", "Sepal"),
              color = c("#EE7733", "#0077BB"),
              symbol = c(1, 3))