This creates a ggplot2 layer that plots the correlation values of the scores for components against the real values, as obtained from dimensionality reduction methods. These methods include principal components analysis and partial least squares.

geom_corr_circle(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", ..., na.rm = FALSE, show.legend = NA,
  outer.linetype = "solid", outer.linecolour = "black",
  outer.linesize = 0.5, inner.linetype = "dotted",
  inner.linecolour = "black", inner.linesize = 0.5,
  center.linetype = "solid", center.linecolour = "grey50",
  center.linesize = 0.3, inherit.aes = TRUE)

Arguments

mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame., and will be used as the layer data.

stat

The statistical transformation to use on the data for this layer, as a string.

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

...

other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like color = "red" or size = 3. They may also be parameters to the paired geom/stat.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

outer.linetype, outer.linecolour, outer.linesize

The line type, colour, and size for the outer circle line.

inner.linetype, inner.linecolour, inner.linesize

The line type, colour, and size for the inner circle line.

center.linetype, center.linecolour, center.linesize

The line type, colour, and size for the two center lines.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

Value

Adds a ggplot2 geom layer.

Examples

library(ggplot2) library(broom) library(pls)
#> #> Attaching package: ‘pls’
#> The following object is masked from ‘package:stats’: #> #> loadings
data(yarn) # Set up data to plot. fit <- plsr(density ~ NIR, 6, data = yarn, validation = "CV") fit <- cor(model.matrix(fit), scores(fit)[, 1:2, drop = FALSE]) fit <- as.data.frame(fit) fit$Variables <- rownames(fit) rownames(fit) <- NULL colnames(fit)[1:2] <- c("Comp1", "Comp2") fit$Groups <- as.factor(sample.int(2, nrow(fit), replace = TRUE)) p <- ggplot(fit, aes(x = Comp1, y = Comp2)) p + geom_corr_circle()
p + geom_corr_circle(outer.linetype = "dotted")
p + geom_corr_circle(inner.linecolour = "blue")
p + geom_corr_circle(center.linesize = 1)
p + geom_corr_circle(center.linecolour = "grey50", size = 3)
# If you want to remove the circle or center lines, use 0. p + geom_corr_circle(center.linetype = 0)
p + geom_corr_circle(outer.linetype = 0)
# With grouping p + geom_corr_circle(aes(colour = Groups))
p + geom_corr_circle(aes(colour = Groups), size = 3) + scale_colour_brewer()
p + geom_corr_circle(aes(alpha = Groups))
#> Warning: Using alpha for a discrete variable is not advised.
p + geom_corr_circle(aes(size = Groups))
#> Warning: Using size for a discrete variable is not advised.
p + geom_corr_circle(aes(shape = Groups), size = 2)
# With facets p + geom_corr_circle() + facet_grid(~ Groups)