pcadf: Function to run a PCA, plot and optionally return the data...

In pcvr: Plant Phenotyping and Bayesian Statistics

Function to run a PCA, plot and optionally return the data with PCA coordinates and pca object

Description

Function to run a PCA, plot and optionally return the data with PCA coordinates and pca object

Usage

pcadf(
  df = NULL,
  cols = NULL,
  color = NULL,
  facet = NULL,
  returnData = TRUE,
  ncp = NULL
)

Arguments

df	Dataframe to ordinate
cols	columns to reduce dimensions of. Can be specified with names or positions. If this is length of 1 then it is treated as regex pattern to match the column names that should be used.
color	column name(s) used to color points in the pca plot.
facet	Optional column or vector to facet plots on.
returnData	Logical, should data be returned?
ncp	Optional, number of principal components to return attached to dataframe if data is returned. Defaults to all.

Details

If data is returned then it will contain the coordinates from the PCA and will not contain the columns that were reduced.

Value

A ggplot or list with a ggplot, a dataframe with the data and PCs, and the factominer PCA object as elements.

Examples

dists <- list(
  rlnorm = list(meanlog = log(40), sdlog = 0.5),
  rnorm = list(mean = 60, sd = 10)
)
mv <- mvSim(
  dists = dists, n_samples = 100, counts = 1000,
  min_bin = 1, max_bin = 180, wide = TRUE
)
mv$otherGroup <- sample(c("a", "b"), size = nrow(mv), replace = TRUE)
pcadf(mv, cols = "sim_", returnData = TRUE)
pcadf(mv, cols = 2:181, color = c("group", "otherGroup"), returnData = FALSE)

pcvr documentation built on April 16, 2025, 5:12 p.m.