hier.part {hier.part} | R Documentation |
Partitions variance in a multivariate dataset
hier.part(y, xcan, family = "gaussian", gof = "RMSPE", barplot = TRUE)
y |
a vector containing the dependent variables |
xcan |
a dataframe containing the n independent variables |
family |
family argument of glm
|
gof |
Goodness-of-fit measure. Currently "RMSPE", Root-mean-square 'prediction' error, "logLik", Log-Likelihood or "Rsqu", R-squared |
barplot |
If TRUE, a barplot of I and J for each variable is plotted expressed as percentage of total explained variance. |
This function calculates goodness of fit measures for the entire
hierarchy of models using all combinations of N independent variables
using the function all.regs
. It takes the list of goodness
of fit measures and, using the partition
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).
At this stage, the partition routine will not run for more than 12 independent variables.
a list containing
gfs |
a data frame or vector 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 |
Chris Walsh Chris.Walsh@sci.monash.edu.au using c and fortran code written by Ralph MacNally Ralph.MacNally@sci.monash.edu.au.
Chevan, A. and Sutherland, M. (1991) Hierarchical Partitioning. The American Statistician 45: 90-96. Mac Nally, R. (2000) Regression and model building in conservation biology, biogeography and ecology: the distinction between and reconciliation of 'predictive' and 'explanatory' models. Biodiversity and Conservation 9: 655-671.
#linear regression with four independent variables data(urban) env <- urban[,3:6] hier.part(urban$chl, env, fam = "gaussian", gof = "Rsqu") #logistic regression with four independent variables data(urban1) env1 <- urban1[,2:5] hier.part(urban1$pa, env1, fam = "binomial", gof = "logLik")