qq.evaluate.core {EvaluateCore} | R Documentation |
Quantile-Quantile Plots
Description
Plot Quantile-Quantile (QQ) plots (Wilk and Gnanadesikan 1968) to graphically compare the probability distributions of quantitative traits between entire collection (EC) and core set (CS).
Usage
qq.evaluate.core(data, names, quantitative, 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. |
selected |
Character vector with the names of individuals selected in
core collection and present in the |
Value
A list with the ggplot
objects of QQ plots of CS vs EC for
each trait specified as quantitative
.
References
Wilk MB, Gnanadesikan R (1968). “Probability plotting methods for the analysis for the analysis of data.” Biometrika, 55(1), 1–17.
See Also
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)))
qq.evaluate.core(data = ec, names = "genotypes",
quantitative = quant, selected = core)