predict.glmrob {robustbase} | R Documentation

## Predict Method for Robust GLM ("glmrob") Fits

### Description

Obtains predictions and optionally estimates standard errors of those predictions from a fitted robust generalized linear model (GLM) object.

### Usage

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

### Arguments

`object` | a fitted object of class inheriting from `"glmrob"`.
`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. The `"terms"` option returns a matrix giving the fitted values of each term in the model formula on the linear predictor scale.

The value of this argument can be abbreviated.

`se.fit` | logical switch indicating if standard errors are required.
`dispersion` | the dispersion of the GLM fit to be assumed in computing the standard errors. If omitted, that returned by `summary` applied to the object is used.
`terms` | with `type="terms"` by default all terms are returned. A character vector specifies which terms are to be returned
`na.action` | function determining what should be done with missing values in `newdata`. The default is to predict `NA`.
`...` | optional further arguments, currently simply passed to `predict.lmrob()`.

### Value

If `se = FALSE`, a vector or matrix of predictions. If `se = TRUE`, a list with components

`fit` | Predictions
`se.fit` | Estimated standard errors
`residual.scale` | A scalar giving the square root of the dispersion used in computing the standard errors.

### Author(s)

Andreas Ruckstuhl

### See Also

`glmrob()` to fit these robust GLM models, `residuals.glmrob()` and other methods; `predict.lm()`, the method used for a non-robust fit.

### Examples

    data(carrots)
    ## simplistic testing & training:
    i.tr <- sample(24, 20)
    fm1 <- glmrob(cbind(success, total-success) ~ logdose + block,
                  family = binomial, data = carrots, subset = i.tr)
    fm1
    predict(fm1, carrots[-i.tr, ]) # --> numeric vector
    predict(fm1, carrots[-i.tr, ],
            type="response", se = TRUE)# -> a list

    data(vaso)
    Vfit <- glmrob(Y ~ log(Volume) + log(Rate), family=binomial, data=vaso)
    newd <- expand.grid(Volume = (V. <- seq(.5, 4, by = 0.5)),
                        Rate   = (R. <- seq(.25,4, by = 0.25)))
    p <- predict(Vfit, newd)
    filled.contour(V., R., matrix(p, length(V.), length(R.)),
          main = "predict(glmrob(., data=vaso))", xlab="Volume", ylab="Rate")

[Package robustbase version 0.92-8 Index]