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Interquartile Range

Source: R/iqr.evaluate.core.R
iqr.evaluate.core.Rd

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 accession count (excluding missing data) as well as 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.

See also

IQR

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)))

iqr.evaluate.core(data = ec, names = "genotypes",
                  quantitative = quant, selected = core)
#>    Trait Count    EC_IQR    CS_IQR
#> 1   NMSR  1684 10.000000  9.250000
#> 2   TTRN  1684  2.500000  2.666667
#> 3  TFWSR  1684  4.800000  5.550000
#> 4   TTRW  1684  1.500000  2.266667
#> 5  TFWSS  1684  7.400000  8.300000
#> 6   TTSW  1684  2.200000  2.837500
#> 7   TTPW  1684 11.250000 13.650000
#> 8   AVPW  1684  3.470833  5.110000
#> 9   ARSR  1684  3.000000  3.000000
#> 10  SRDM  1684  6.000000  4.625000