R: Comparison of Relative Importances in a Multinomial Logit...

relrelimp {relimp} | R Documentation

## 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

### See Also

`relimp`

### 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.

[Package relimp version 1.0-5 Index]