Quantile-Quantile Plots

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,
  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

#>

Usage

Arguments

Value

References

See also

Examples