relimp: relrelimp – R documentation

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relrelimp

Comparison of Relative Importances in a Multinomial Logit Model

Description

Produces a summary of the relative importance of two predictors or two sets of predictors in a fitted multinom model object, and compares relative importances across two of the fitted logit models.

Usage

relrelimp(object, set1=NULL, set2=NULL, label1="set1", label2="set2", 
          subset=TRUE, 
          response.cat1=NULL, response.cat2=NULL)

Arguments

`object`	A model object of class `multinom`
`set1`	An index or vector of indices for the effects to be included in the numerator of the comparison
`set2`	An index or vector of indices for the effects to be included in the denominator of the comparison
`label1`	A character string; mnemonic name for the variables in `set1`
`label2`	A character string; mnemonic name for the variables in `set2`
`subset`	Either a vector of numeric indices for the cases to be included in the standardization of effects, or a vector of logicals (`TRUE` for inclusion) whose length is the same as the number of rows in the model frame, `object$model`. The default choice is to include all cases in the model frame.
`response.cat1`	A character string used to specify the first regression of interest (i.e., the regression which predicts the log odds on `response.cat1` versus the model's reference category). The `response.cat1` argument should be an element of `object$lab`.
`response.cat2`	A character string used to specify the second regression of interest (i.e., the regression which predicts the log odds on `response.cat2` versus the model's reference category). The `response.cat2` argument should be an element of `object$lab`.

Details

Computes a relative importance summary as described in relimp, for each of the two regressions specified by response.cat1 and response.cat2 (relative to the same reference category); and computes the difference of those two relative importance summaries, along with an estimated standard error for that difference.

Value

An object of class relrelimp, with at least the following components:

`model`	The call used to construct the model object summarized
`sets`	The two sets of indices specified as arguments
`response.category`	A character vector containing the specified `response.cat1` and `response.cat2`
`log.ratio`	The natural logarithm of the ratio of effect standard deviations corresponding to the two sets specified. A vector with three components: the first is for `response.cat1` versus the reference category, the second for `response.cat2` versus the reference category, the third is the difference.
`se.log.ratio`	Estimated standard errors for the elements of `log.ratio`

Author(s)

David Firth, d.firth@warwick.ac.uk

Examples

##  Data on housing and satisfaction, from Venables and Ripley
library(MASS)
library(nnet)
data(housing)
house.mult <- multinom(Sat ~ Infl + Type + Cont, weights = Freq,
  data = housing)
relrelimp(house.mult, set1 = 2:3, set2 = 7, 
                      label1 = "Influence", label2 = "Contact",
                      response.cat1 = "Medium", response.cat2 = "High")
## Computes the relative contribution of Influence and Contact in 
## each of the two logistic regressions (Med/Low and High/Low), and
## compares those two relative-contribution measures.

relimp

Relative Contribution of Effects in a Regression Model

v1.0-5

GPL (>= 2)

Authors

David Firth with contributions from Heather Turner and John Fox

Initial release

2016-03-19