pca.evaluate.core: Principal Component Analysis

In EvaluateCore: Quality Evaluation of Core Collections

Principal Component Analysis

Description

Compute Principal Component Analysis Statistics \insertCitemardia_multivariate_1979EvaluateCore to compare the probability distributions of quantitative traits between entire collection (EC) and core set (CS).

Usage

pca.evaluate.core(
  data,
  names,
  quantitative,
  selected,
  center = TRUE,
  scale = TRUE,
  npc.plot = 6
)

Arguments

data	The data as a data frame object. The data frame should possess one row per individual and columns with the individual names and multiple trait/character data.
names	Name of column with the individual names as a character string
quantitative	Name of columns with the quantitative traits as a character vector.
selected	Character vector with the names of individuals selected in core collection and present in the names column.
center	either a logical value or numeric-alike vector of length equal to the number of columns of x, where ‘numeric-alike’ means that as.numeric(.) will be applied successfully if is.numeric(.) is not true.
scale	either a logical value or a numeric-alike vector of length equal to the number of columns of x.
npc.plot	The number of principal components for which eigen values are to be plotted. The default value is 6.

Value

A list with the following components.

EC PC Importance	A data frame of importance of principal components for EC
EC PC Loadings	A data frame with eigen vectors of principal components for EC
CS PC Importance	A data frame of importance of principal components for CS
CS PC Loadings	A data frame with eigen vectors of principal components for CS
Scree Plot	The scree plot of principal components for EC and CS as a ggplot object.
PC Loadings Plot	A plot of the eigen vector values of principal components for EC and CS as specified by npc.plot as a ggplot2 object.

References

\insertAllCited

See Also

prcomp

Examples

data("cassava_CC")
data("cassava_EC")

ec <- cbind(genotypes = rownames(cassava_EC), cassava_EC)
ec$genotypes <- as.character(ec$genotypes)
rownames(ec) <- NULL

core <- rownames(cassava_CC)

quant <- c("NMSR", "TTRN", "TFWSR", "TTRW", "TFWSS", "TTSW", "TTPW", "AVPW",
           "ARSR", "SRDM")
qual <- c("CUAL", "LNGS", "PTLC", "DSTA", "LFRT", "LBTEF", "CBTR", "NMLB",
          "ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC",
          "PSTR")

ec[, qual] <- lapply(ec[, qual],
                     function(x) factor(as.factor(x)))

pca.evaluate.core(data = ec, names = "genotypes",
                  quantitative = quant, selected = core,
                  center = TRUE, scale = TRUE, npc.plot = 4)

EvaluateCore documentation built on July 3, 2022, 5:06 p.m.