ordChange {DAMisc} | R Documentation |
For objects of class polr
, it calculates the change in predicted
probabilities, for maximal discrete changes in all covariates holding all
other variables constant at typical values.
ordChange(
obj,
data,
typical.dat = NULL,
diffchange = c("range", "sd", "unit"),
n = 1,
sim = TRUE,
R = 1500
)
obj |
A model object of class |
data |
Data frame used to fit |
typical.dat |
Data frame with a single row containing values at which
to hold variables constant when calculating first differences. These values
will be passed to |
diffchange |
A string indicating the difference in predictor values to
calculate the discrete change. |
n |
Number of |
sim |
Logical indicating whether or not simulations should be done to generate confidence intervals for the difference. |
R |
Number of simulations. |
The function calculates the changes in predicted probabilities for maximal
discrete changes in the covariates for objects of class polr
. This
function works with polynomials specified with the poly
function. It
also works with multiplicative interactions of the covariates by virtue of
the fact that it holds all other variables at typical values. By default,
typical values are the median for quantitative variables and the mode for
factors. The way the function works with factors is a bit different. The
function identifies the two most different levels of the factor and
calculates the change in predictions for a change from the level with the
smallest prediction to the level with the largest prediction.
A list with the following elements:
diffs |
A matrix of calculated first differences |
minmax |
A matrix of values that were used to calculate the predicted changes |
minPred |
A matrix of predicted probabilities when each variable is held at its minimum value, in turn. |
maxPred |
A matrix of predicted probabilities when each variable is held at its maximum value, in turn. |
Dave Armstrong
library(MASS)
data(france)
polr.mod <- polr(vote ~ age + male + retnat + lrself, data=france)
typical.france <- data.frame(
age = 35,
retnat = factor(1, levels=1:3, labels=levels(france$retnat)),
stringsAsFactors=TRUE)
ordChange(polr.mod, data=france, typical.dat=typical.france, sim=FALSE)