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Takes a PCA object returned by prcomp(), extracts the standard deviations of the principal components (PC), and plots these against the PC number. The guidance line assumes that all of the variables have been standardised prior to PCA.

Usage

ggscree(pc, q = 2, guide = TRUE, cumulative = FALSE)

Arguments

pc

PCA object

q

number of principal components to show, default 2 (you should change)

guide

logical whether to compute and add a typical value of the variance, if the data was full-dimensional

cumulative

logical whether to draw cumulative variance

Value

scree a ggplot object

Examples

data(aflw)
aflw_std <- apply(aflw[,7:35], 2, function(x)
                    (x-mean(x, na.rm=TRUE))/
                     sd(x, na.rm=TRUE))
aflw_pca <- prcomp(aflw_std[,c("goals","behinds",
                               "kicks","disposals")])
ggscree(aflw_pca, q=3)