iqr.evaluate.core {EvaluateCore} | R Documentation |
Compute the Interquartile Range (IQR) (Upton and Cook 1996) to compare quantitative traits of the entire collection (EC) and core set (CS).
iqr.evaluate.core(data, names, quantitative, selected)
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 |
A data frame with the IQR values of the EC and CS for the traits
specified as quantitative
.
Upton G, Cook I (1996). “General summary statistics.” In Understanding statistics. Oxford University Press.
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# 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
####################################
iqr.evaluate.core(data = ec, names = "genotypes",
quantitative = quant, selected = core)