predict.jmdem: Predict Method for JMDEM Fits

In jmdem: Fitting Joint Mean and Dispersion Effects Models

Description

Obtains predictions and optionally estimates standard errors of those predictions from a fitted joint mean and dispersion effect model object.

Usage

## S3 method for class 'jmdem'
predict(object, newdata = NULL, type = c("link", "response"), 
        se.fit = FALSE, na.action = na.pass, ...)

Arguments

object	a fitted object of class inheriting from "jmdem".
newdata	optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used.
type	the type of prediction required. The default is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Thus for a default binomial model the default predictions are of log-odds (probabilities on logit scale) and type = "response" gives the predicted probabilities.
se.fit	logical switch indicating if standard errors are required.
na.action	function determining what should be done with missing values in newdata. The default is to predict NA.
...	further arguments passed to or from other methods.

Details

If newdata is omitted the predictions are based on the data used for the fit. In that case how cases with missing values in the original fit is determined by the na.action argument of that fit. If na.action = na.omit omitted cases will not appear in the residuals, whereas if na.action = na.exclude they will appear (in predictions and standard errors), with residual value NA. See also napredict.

Value

If se.fit = FALSE, a vector or matrix of predictions.

If se.fit = TRUE, a list with components

fit	Predictions, as for se.fit = FALSE.
se.fit	Estimated standard errors.

Note

Variables are first looked for in newdata and then searched for in the usual way (which will include the environment of the formula used in the fit). A warning will be given if the variables found are not of the same length as those in newdata if it was supplied.

Author(s)

Karl Wu Ka Yui ([e-mail])

See Also

jmdem

Examples

## Example in jmdem(...)
MyData <- simdata.jmdem.sim(mformula = y ~ x, dformula = ~ z, 
                            mfamily = poisson(), 
                            dfamily = Gamma(link = "log"), 
                            beta.true = c(0.5, 4), 
                            lambda.true = c(2.5, 3), n = 100)
                            
fit <- jmdem(mformula = y ~ x, dformula = ~ z, data = MyData, 
             mfamily = poisson, dfamily = Gamma(link = "log"), 
             dev.type = "deviance", method = "CG")
              
## Predict on the scale of the response variable with standard errors.
predict(fit, type = "response", se.fit = TRUE)

## Predict based on a new observation on the scale of the linear 
## predictors with standard errors.
predict(fit, newdata = data.frame(x = -1.5, z = 100), se.fit = TRUE)

jmdem documentation built on March 13, 2020, 2:20 a.m.