Autoplots for gt_pca objects — autoplot_gt

For gt_pca, the following types of plots are available:

screeplot: a plot of the eigenvalues of the principal components (currently it plots the singular value)
scores a scatterplot of the scores of each individual on two principal components (defined by pc)
loadings a plot of loadings of all loci for a given component (chosen with pc)

Usage

# S3 method for class 'gt_pca'
autoplot(object, type = c("screeplot", "scores", "loadings"), k = NULL, ...)

Arguments

object: an object of class gt_pca
type: the type of plot (one of "screeplot", "scores" and "loadings")
k: the principal components to be plotted: for scores, a pair of values e.g. c(1,2); for loadings either one or more values.
...: not currently used.

Value

a ggplot2 object

Details

autoplot produces simple plots to quickly inspect an object. They are not customisable; we recommend that you use ggplot2 to produce publication ready plots.

Examples

library(ggplot2)
# Create a gen_tibble of lobster genotypes
bed_file <-
  system.file("extdata", "lobster", "lobster.bed", package = "tidypopgen")
lobsters <- gen_tibble(bed_file,
  backingfile = tempfile("lobsters"),
  quiet = TRUE
)

# Remove monomorphic loci and impute
lobsters <- lobsters %>% select_loci_if(loci_maf(genotypes) > 0)
lobsters <- gt_impute_simple(lobsters, method = "mode")

# Create PCA object
pca <- gt_pca_partialSVD(lobsters)

# Screeplot
autoplot(pca, type = "screeplot")


# Scores plot
autoplot(pca, type = "scores")


# Colour by population
autoplot(pca, type = "scores") + aes(colour = lobsters$population)


# Scores plot of different components
autoplot(pca, type = "scores", k = c(1, 3)) +
  aes(colour = lobsters$population)