partition {hier.part}R Documentation

Hierarchical partitioning from a list of goodness of fit measures

Description

Partitions variance in a multivariate dataset from a list of goodness of fit measures

Usage

partition(gfs, pcan, var.names = NULL)

Arguments

gfs an array as outputted by the function all.regs or a vector of goodness of fit measures from a hierarchy of regressions based on pcan variables in ascending order (as produced by function combos, but also including the null model as the first element)
pcan the number of variables from which the hierarchy was constructed (maximum = 12)
var.names an array of pcan variable names, if required

Details

This function applies the hierarchical partitioning algorithm of Chevan and Sutherland (1991) to return a simple table listing of each variable, its independent contribution (I) and its conjoint contribution with all other variables (J). The output is identical to the function hier.part, which takes the dependent and independent variable data as its input

At this stage, the partition routine will not run for more than 12 independent variables.

Value

a list containing

gfs a data frame listing all combinations of independent variables in the first column in ascending order, and the corresponding goodness of fit measure for the model using those variables
IJ a data frame of I, the independent and J the joint contribution for each independent variable
IJ.perc a data frame of I and J expressed as percentage of total explained variance

Author(s)

Chris Walsh Chris.Walsh@sci.monash.edu.au using c and fortran code written by Ralph MacNally Ralph.MacNally@sci.monash.edu.au.

References

Chevan, and Sutherland (1991) The American Statistician 45: 90

Examples

           #linear regression with four independent variables
           data(urban)
           env <- urban[,3:6]
           gofs <- all.regs(urban$chl, env,
                            fam = "gaussian", gof = "Rsqu")
           partition(gofs, pcan = 4, var.names = names(urban[,3:6]))

           #hierarchical partitioning of logistic and linear regression
           #goodness of fit measures from Chevan and Sutherland (1991) 
           data(chevan)
           partition(chevan$chisq, pcan = 4)
           partition(chevan$rsqu, pcan = 4)

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