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.

Examples

Run this code

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"))

Run the code above in your browser using DataLab

Hear from leaders powering AI transformation