# rcontrib

0th

Percentile

##### Computes a measure of how correlated each variable in a set is with the other variable, conditional on a nominated subset of them

A measure of how correlated a variable is with those in a set is given by the square root of the sum of squares of the correlation coefficients between the variables and the other variables in the set (Cummings, 2007). Here, the partial correlation between the subset of the variables listed in response that are not listed in include is calculated from the partial correlation matrix for the subset, adjusting for those variables in include. This is useful for manually deciding which of the variables not in include should next be added to it.

Keywords
manip, data
##### Usage
rcontrib(responses, data, include = NULL)
##### Arguments
responses

A character giving the names of the columns in data from which the correlation measure is to be calculated.

data

A data.frame containing the columns of variables from which the correlation measure is to be calculated.

include

A character giving the names of the columns in data for the variables for which other variables are to be adjusted.

##### Value

A numeric giving the correlation measures.

##### References

Cumming, J. A. and D. A. Wood (2007) Dimension reduction via principal variables. Computational Statistics and Data Analysis, 52, 550--565.

PVA, intervalPVA

• rcontrib
##### Examples
# NOT RUN {
data(exampleData)
responses <- c("Area","Area.SV","Area.TV", "Image.Biomass", "Max.Height","Centre.Mass",
"Density", "Compactness.TV", "Compactness.SV")
h <-  rcontrib(responses, longi.dat, include = "Area")
# }

Documentation reproduced from package growthPheno, version 1.0-13, License: GPL (>= 2)

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