corr.evaluate.core {EvaluateCore}R Documentation

Phenotypic Correlations

Description

Compute phenotypic correlations (Pearson 1895) between traits, plot correlation matrices as correlograms (Friendly 2002) and calculate mantel correlation (Legendre and Legendre 2012) between them to compare entire collection (EC) and core set (CS).

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 (\(r\)) between EC and CS phenotypic correlation matrices, it's p value and significance (*: p \(\leq\) 0.01; **: p \(\leq\) 0.05; ns: p \( > \) 0.05).

References

Friendly M (2002). “Corrgrams.” The American Statistician, 56(4), 316–324.

Legendre P, Legendre L (2012). “Interpretation of ecological structures.” In Developments in Environmental Modelling, volume 24, 521–624. Elsevier.

Pearson K (1895). “Note on regression and inheritance in the case of two parents.” Proceedings of the Royal Society of London, 58, 240–242.

See Also

cor, cor_pmat ggcorrplot, mantel

Examples


####################################
# Use data from R package ccChooser
####################################

library(ccChooser)
data("dactylis_CC")
data("dactylis_EC")

ec <- cbind(genotypes = rownames(dactylis_EC), dactylis_EC[, -1])
ec$genotypes <- as.character(ec$genotypes)
rownames(ec) <- NULL
ec[, c("X1", "X6", "X7")] <- lapply(ec[, c("X1", "X6", "X7")],
                                    function(x) cut(x, breaks = 4))
ec[, c("X1", "X6", "X7")] <- lapply(ec[, c("X1", "X6", "X7")],
                                    function(x) factor(as.numeric(x)))
head(ec)

core <- rownames(dactylis_CC)

quant <- c("X2", "X3", "X4", "X5", "X8")
qual <- c("X1", "X6", "X7")

####################################
# EvaluateCore
####################################

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



[Package EvaluateCore version 0.1.2 Index]