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

In relimp: Relative Contribution of Effects in a Regression 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, [e-mail]

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.

Example output

# weights:  24 (14 variable)
initial  value 1846.767257 
iter  10 value 1747.045232
final  value 1735.041933 
converged
$model
multinom(formula = Sat ~ Infl + Type + Cont, data = housing, 
    weights = Freq)

$response.category
[1] "Medium" "High"  

$dispersion
NULL

$sets
$sets$Influence
[1] "InflMedium" "InflHigh"  

$sets$Contact
[1] "ContHigh"


$log.ratio
[1]  0.4276920  1.0066176 -0.5789257

$se.log.ratio
[1] 0.4232093 0.2639887 0.3766682

attr(,"class")
[1] "relrelimp"

relimp documentation built on May 2, 2019, 2:02 p.m.