corr.evaluate.core: Phenotypic Correlations

In EvaluateCore: Quality Evaluation of Core Collections

Phenotypic Correlations

Description

Compute phenotypic correlations \insertCitepearson_note_1895EvaluateCore between traits, plot correlation matrices as correlograms \insertCitefriendly_corrgrams_2002EvaluateCore and calculate mantel correlation \insertCitelegendre_interpretation_2012EvaluateCore between them to compare entire collection (EC) and core set (CS). \loadmathjax

Usage

corr.evaluate.core(data, names, quantitative, qualitative, selected)

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.
qualitative	Name of columns with the qualitative traits as a character vector.
selected	Character vector with the names of individuals selected in core collection and present in the names column.

Value

A list with the following components.

Correlation Matrix	The matrix with phenotypic correlations between traits in EC (below diagonal) and CS (above diagonal).
Correologram	A correlogram of phenotypic correlations between traits in EC (below diagonal) and CS (above diagonal) as a ggplot object.
Mantel Correlation	A data frame with Mantel correlation coefficient (\mjseqnr) between EC and CS phenotypic correlation matrices, it's p value and significance (*: p \mjseqn\leq 0.01; **: p \mjseqn\leq 0.05; ns: p \mjseqn > 0.05).

References

\insertAllCited

See Also

cor, cor_pmat ggcorrplot, mantel

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)))

corr.evaluate.core(data = ec, names = "genotypes", quantitative = quant,
                   qualitative = qual, selected = core)

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