growthPheno (version 3.1.12)

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

Description

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 (Cumming and Wooff, 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.

Usage

# S3 method for matrix
rcontrib(obj, responses, include = NULL, ...)

Value

A numeric giving the correlation measures.

Arguments

obj

A matrix containing the correlations of the variables from which the correlation measure is to be calculated.

responses

A character giving the names of the columns in data 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.

...

allows passing of arguments to other functions.

Author

Chris Brien

References

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

See Also

rcontrib, rcontrib.data.frame, PVA, intervalPVA.data.frame

Examples

data(exampleData)
longi.dat <- prepImageData(data=raw.dat, smarthouse.lev=1)
longi.dat <- within(longi.dat, 
                    {
                      Max.Height <- pmax(Max.Dist.Above.Horizon.Line.SV1,  
                                         Max.Dist.Above.Horizon.Line.SV2)
                      Density <- PSA/Max.Height
                      PSA.SV = (PSA.SV1 + PSA.SV2) / 2
                      Image.Biomass = PSA.SV * (PSA.TV^0.5)
                      Centre.Mass <- (Center.Of.Mass.Y.SV1 + Center.Of.Mass.Y.SV2) / 2
                      Compactness.SV = (Compactness.SV1 + Compactness.SV2) / 2
                    })
responses <- c("PSA","PSA.SV","PSA.TV", "Image.Biomass", "Max.Height","Centre.Mass",
               "Density", "Compactness.TV", "Compactness.SV")
R <- Hmisc::rcorr(as.matrix(longi.dat[responses]))$r
h <- rcontrib(R, responses, include = "PSA")