Partial correlation
This function calculates the partial correlation matrix between a set of traits and a third control variable.
cor.par(traits, c.trait, trait.names = FALSE, silent=FALSE)
traits |
a dataframe or a matrix object containing traits as columns and individuals as rows |
c.trait |
a vector containing the third control variable to estimate partial correlations, for instance: organ size or individual size |
trait.names |
a logical. If 'FALSE' (default), trait names in the output are defined as "Trait 1", "Trait 2", ..., "Trait n". If 'TRUE', output trait names are the same than those in the input dataframe |
silent |
a logical. If 'FALSE' (default), return a warning message if any row contains missing data (and is removed from the original dataset before computation). |
This function is utilised internally by pintsc and pintsc.boot to estimate the phenotypic integration index from partial correlations. Rows containing missing values are removed.
A matrix with the pairwise partial correlation between traits.
R. Torices, A.J. Muñoz-Pajares
# Dataset from Torices & Méndez (2014) # This data set represents the dry mass (in grams) of inflorescence components of the sunflower # species Tussilago farfara. The inflorescences were dissected in 'SCAPE', 'RECEPTACLE', 'MALEFL' # (male flowers), 'OVAR' (reproductive part of female flowers), and 'RAYS' (the petaloid ray of # female flower). Furthermore in the last column the total weight of the inflorescence is added # 'TOTALSIZE' data(tussilago) print(tussilago) cor.par(tussilago[,1:5], tussilago[,6], trait.names=TRUE) # NA automatically removed traits<-na.exclude(tussilago) # NA manually removed cor.par(traits[,1:5], traits[,6], trait.names=TRUE)
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