iqr.evaluate.core {EvaluateCore} R Documentation

## Interquartile Range

### Description

Compute the Interquartile Range (IQR) (Upton and Cook 1996) to compare quantitative traits of the entire collection (EC) and core set (CS).

### Usage

iqr.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 names column.

### Value

A data frame with the IQR values of the EC and CS for the traits specified as quantitative.

### References

Upton G, Cook I (1996). “General summary statistics.” In Understanding statistics. Oxford University Press.

IQR

### 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)))

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)



[Package EvaluateCore version 0.1.2 Index]