all.regs {hier.part}R Documentation

Goodness of fit measures for a regression hierarchy

Description

Calculates goodness of fit measures for regressions of a single dependent variable to all combinations of N independent variables

Usage

all.regs(y, xcan, family = "gaussian", gof = "RMSPE", print.vars = FALSE)

Arguments

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
print.vars if FALSE, the function returns a vector of goodness-of-fit measures. If TRUE, a data frame is returned with first column listing variable combinations and the second column listing goodness-of-fit measures.

Details

This function calculates goodness of fit measures for the entire hierarchy of models using all combinations of N dependent variables, and returns them as an ordered list ready for input into the function partition. This function requires the gtools package in the gregmisc bundle

Value

gfs If print.vars is FALSE, a vector of goodness of fit measures for all combinations of independent varaiables in the hierarchy or, if print.vars is TRUE, 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

Author(s)

Chris Walsh Chris.Walsh@sci.monash.edu.au.

References

Hatt, B. E., Fletcher, T. D., Walsh, C. J. and Taylor, S. L. 2004 The influence of urban density and drainage infrastructure on the concentrations and loads of pollutants in small streams. Environmental Management 34, 112–124.

Walsh, C. J., Papas, P. J., Crowther, D., Sim, P. T., and Yoo, J. 2004 Stormwater drainage pipes as a threat to a stream-dwelling amphipod of conservation significance, Austrogammarus australis, in southeastern Australia. Biodiversity and Conservation 13, 781–793.

See Also

hier.part, partition, rand.hp

Examples

           #linear regression of log(electrical conductivity) in streams
           #against seven independent variables describing catchment
           #characteristics (from Hatt et al. 2004)
           data(urbanwq)
           env <- urbanwq[,2:8]
           all.regs(urbanwq$lec, env, fam = "gaussian", gof = "Rsqu",
           print.vars = TRUE)

           #logistic regression of an amphipod species occurrence in
           #streams against four independent variables describing
           #catchment characteristics (from Walsh et al. 2004)
           data(amphipod)
           env1 <- amphipod[,2:5]
           all.regs(amphipod$australis, env1, fam = "binomial",
           gof = "logLik", print.vars = TRUE)

[Package hier.part version 1.0-3 Index]