PCA visualization (individual and/or variable factor map(s)) for a PLNPCAfit object

PCA visualization (individual and/or variable factor map(s)) for a PLNPCAfit object

# S3 method for PLNPCAfit
plot(
  x,
  map = c("both", "individual", "variable"),
  nb_axes = min(3, x$rank),
  axes = seq.int(min(2, x$rank)),
  ind_cols = "ind_colors",
  var_cols = "var_colors",
  plot = TRUE,
  main = NULL,
  ...
)

Arguments

x

an R6 object with class PLNPCAfit

map

the type of output for the PCA visualization: either "individual", "variable" or "both". Default is "both".

nb_axes

scalar: the number of axes to be considered when map = "both". The default is min(3,rank).

axes

numeric, the axes to use for the plot when map = "individual" or map = "variable". Default it c(1,min(rank))

ind_cols

a character, factor or numeric to define the color associated with the individuals. By default, all variables receive the default color of the current palette.

var_cols

a character, factor or numeric to define the color associated with the variables. By default, all variables receive the default color of the current palette.

plot

logical. Should the plot be displayed or sent back as ggplot object

main

character. A title for the single plot (individual or variable factor map). If NULL (the default), an hopefully appropriate title will be used.

...

Not used (S3 compatibility).

Value

displays an individual and/or variable factor maps for the corresponding axes, and/or sends back a ggplot or gtable object

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPCAs <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:5)
#> 
#>  Initialization...
#> 
#>  Adjusting 5 PLN models for PCA analysis.
#> 	 Rank approximation = 5 
	 Rank approximation = 2 
	 Rank approximation = 1 
	 Rank approximation = 3 
	 Rank approximation = 4 

#>  Post-treatments
#>  DONE!
myPCA <- getBestModel(myPCAs)
if (FALSE) {
plot(myPCA, map = "individual", nb_axes=2, ind_cols = trichoptera$Group)
plot(myPCA, map = "variable", nb_axes=2)
plot(myPCA, map = "both", nb_axes=2, ind_cols = trichoptera$Group)
}