View source: R/pca.evaluate.core.R
pca.evaluate.core | R Documentation |
Compute Principal Component Analysis Statistics \insertCitemardia_multivariate_1979EvaluateCore to compare the probability distributions of quantitative traits between entire collection (EC) and core set (CS).
pca.evaluate.core( data, names, quantitative, selected, center = TRUE, scale = TRUE, npc.plot = 6 )
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 |
center |
either a logical value or numeric-alike vector of length
equal to the number of columns of |
scale |
either a logical value or a numeric-alike vector of length
equal to the number of columns of |
npc.plot |
The number of principal components for which eigen values are to be plotted. The default value is 6. |
A list with the following components.
EC PC Importance |
A data frame of importance of principal components for EC |
EC PC Loadings |
A data frame with eigen vectors of principal components for EC |
CS PC
Importance |
A data frame of importance of principal components for CS |
CS PC Loadings |
A data frame with eigen vectors of principal components for CS |
Scree Plot |
The scree plot of principal components
for EC and CS as a |
PC Loadings Plot |
A plot of
the eigen vector values of principal components for EC and CS as specified
by |
prcomp
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))) pca.evaluate.core(data = ec, names = "genotypes", quantitative = quant, selected = core, center = TRUE, scale = TRUE, npc.plot = 4)
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