# rcontrib.data.frame: Computes a measure of how correlated each variable in a set... In growthPheno: Plotting, Smoothing and Growth Trait Extraction for Longitudinal Data

## 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

 ```1 2``` ```## S3 method for class 'data.frame' rcontrib(obj, responses, include = NULL, ...) ```

## Arguments

 `obj` A `data.frame` containing the columns of 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.

## Value

A `numeric` giving the correlation measures.

Chris Brien

## References

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

`rcontrib`, `rcontrib.matrix`, `PVA`, `intervalPVA.data.frame`
 ```1 2 3 4``` ```data(exampleData) responses <- c("Area","Area.SV","Area.TV", "Image.Biomass", "Max.Height","Centre.Mass", "Density", "Compactness.TV", "Compactness.SV") h <- rcontrib(longi.dat, responses, include = "Area") ```