survey (version 4.4-2)

svyolr: Proportional odds and related models

Description

Fits cumulative link models: proportional odds, probit, complementary log-log, and cauchit.

Usage

svyolr(formula, design, ...)
# S3 method for survey.design2
svyolr(formula, design, start, subset=NULL,...,
    na.action = na.omit,method = c("logistic", "probit", "cloglog", "cauchit"))
# S3 method for svyrep.design
svyolr(formula,design,subset=NULL,...,return.replicates=FALSE, 
    multicore=getOption("survey.multicore"))
# S3 method for svyolr
predict(object, newdata, type = c("class", "probs"), ...)

Value

An object of class svyolr

Arguments

formula

Formula: the response must be a factor with at least three levels

design

survey design object

subset

subset of the design to use; NULL for all of it

...

dots

start

Optional starting values for optimization

na.action

handling of missing values

multicore

Use multicore package to distribute computation of replicates across multiple processors?

method

Link function

return.replicates

return the individual replicate-weight estimates

object

object of class svyolr

newdata

new data for predictions

type

return vector of most likely class or matrix of probabilities

Author

The code is based closely on polr() from the MASS package of Venables and Ripley.

See Also

svyglm, regTermTest

Examples

data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
dclus1<-update(dclus1, mealcat=cut(meals,c(0,25,50,75,100)))

m<-svyolr(mealcat~avg.ed+mobility+stype, design=dclus1)
m

## Use regTermTest for testing multiple parameters
regTermTest(m, ~avg.ed+stype, method="LRT")

## predictions
summary(predict(m, newdata=apiclus2))
summary(predict(m, newdata=apiclus2, type="probs"))