predict.jmdem: Predict Method for JMDEM Fits

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 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.

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.

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.

Examples

Run this code

# NOT RUN {
## 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)

# }