predict.repolr: Predict Method for Fitted repolr Model

In repolr: Repeated Measures Proportional Odds Logistic Regression

Description

Calculates predictions and standard errors of predictions for a fitted repolr model object.

Usage

## S3 method for class 'repolr'
predict(object, newdata = NULL, se.fit = FALSE,
           robust.var = TRUE, type = c("link", "response", "terms"), ...)

Arguments

object	is a model fitted using repolr.
newdata	optionally, a data frame in which to find variables with which to predict; if missing the model fitted values are reported.
se.fit	Logical indicating if standard errors are required.
robust.var	logical; if TRUE, standard errors are based on the robust variance matrix.
type	is the type of prediction required. The default “link” is to use the scale of the linear predictors; i.e. the log-odds of cumulative probabilities. The alternative is to report the predicted cumulative probabilities; “response”. The “terms” option returns the matrix of fitted values for each model term on the scale of the linear predictor.
...	further arguments passed to or from other methods.

Details

If newdata is missing predictions are based on the data used to fit the repolr model. If newdata are supplied then the format of these data must conform to the same format required for model fitting using repolr. See repolr for details.

Value

fit	Predictions.
se.fit	Estimated standard errors.

Examples

data(HHSpain)
mod.0 <- repolr(HHSpain~Sex*Time, data=HHSpain, categories=4, subjects="Patient",
            times=c(1,2,5), corr.mod="uniform", alpha=0.5)
predict(mod.0, newdata = data.frame(Patient = rep(100, 3), Time = c(1, 2, 5),
       Sex = factor(rep(1, 3), levels=1:2, labels=c("F", "M"))), 
       type="link", se.fit = TRUE)

Example output

$fit
     100.1      100.2      100.3      100.4      100.5      100.6      100.7 
 0.4144049  1.4897464  2.7977472  0.2003748  1.2757162  2.5837170 -0.4417157 
     100.8      100.9 
 0.6336257  1.9416266 

$se.fit
    100.1     100.2     100.3     100.4     100.5     100.6     100.7     100.8 
0.3776881 0.3946639 0.4513134 0.3450574 0.3565973 0.4184568 0.4218454 0.4135311 
    100.9 
0.4679883 

repolr documentation built on May 1, 2019, 9:20 p.m.