Quantile-Quantile Plots

Source: R/qq.evaluate.core.R

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).

qq.evaluate.core(
  data,
  names,
  quantitative,
  selected,
  annotate = c("none", "kl", "ks", "ad")
)

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.

annotate

Adds the divergence/distance value between probability distributions of CS and EC as an annotation to the QQ plot. Either "none" (no annotation (Default)) or "kl" (Kullback-Leibler divergence) or "ks" (Kolmogorov-Smirnov distance) or "ad" (Anderson-Darling distance).

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.

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)
#> $NMSR

#> 
#> $TTRN

#> 
#> $TFWSR

#> 
#> $TTRW

#> 
#> $TFWSS

#> 
#> $TTSW

#> 
#> $TTPW

#> 
#> $AVPW

#> 
#> $ARSR

#> 
#> $SRDM

#> 

qq.evaluate.core(data = ec, names = "genotypes",
                 quantitative = quant, selected = core, annotate = "kl")
#> $NMSR

#> 
#> $TTRN

#> 
#> $TFWSR

#> 
#> $TTRW

#> 
#> $TFWSS

#> 
#> $TTSW

#> 
#> $TTPW

#> 
#> $AVPW

#> 
#> $ARSR

#> 
#> $SRDM

#> 

qq.evaluate.core(data = ec, names = "genotypes",
                 quantitative = quant, selected = core, annotate = "ks")
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> $NMSR

#> 
#> $TTRN

#> 
#> $TFWSR

#> 
#> $TTRW

#> 
#> $TFWSS

#> 
#> $TTSW

#> 
#> $TTPW

#> 
#> $AVPW

#> 
#> $ARSR

#> 
#> $SRDM

#> 

qq.evaluate.core(data = ec, names = "genotypes",
                 quantitative = quant, selected = core, annotate = "ad")
#> $NMSR

#> 
#> $TTRN

#> 
#> $TFWSR

#> 
#> $TTRW

#> 
#> $TFWSS

#> 
#> $TTSW

#> 
#> $TTPW

#> 
#> $AVPW

#> 
#> $ARSR

#> 
#> $SRDM

#>