pca.evaluate.core {EvaluateCore}

Principal Component Analysis

Description

Compute Principal Component Analysis Statistics (Mardia et al. 1979) 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

Mardia KV, Kent JT, Bibby JM (1979). Multivariate analysis. Academic Press, London; New York. ISBN 0-12-471250-9 978-0-12-471250-8 0-12-471252-5 978-0-12-471252-2.

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)