R: Predict method for Robust Linear Model ("lmrob") Fits

predict.lmrob {robustbase}

R Documentation

Predict method for Robust Linear Model ("lmrob") Fits

Description

Predicted values based on robust linear model object.

Usage

## S3 method for class 'lmrob'
predict(object, newdata, se.fit = FALSE,
       scale = NULL, df = NULL,
       interval = c("none", "confidence", "prediction"), level = 0.95,
       type = c("response", "terms"), terms = NULL,
       na.action = na.pass, pred.var = res.var/weights, weights = 1, ...)

Arguments

`object`	object of class inheriting from `"lmrob"`
`newdata`	an optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
`se.fit`	a switch indicating if standard errors are required.
`scale`	scale parameter for std.err. calculation
`df`	degrees of freedom for scale
`interval`	type of interval calculation.
`level`	tolerance/confidence level
`type`	Type of prediction (response or model term).
`terms`	if `type="terms"`, which terms (default is all terms)
`na.action`	function determining what should be done with missing values in `newdata`. The default is to predict `NA`.
`pred.var`	the variance(s) for future observations to be assumed for prediction intervals. See ‘Details’.
`weights`	variance weights for prediction. This can be a numeric vector or a one-sided model formula. In the latter case, it is interpreted as an expression evaluated in `newdata`
`...`	further arguments passed to or from other methods.

Value

predict.lmrob produces a vector of predictions or a matrix of predictions and bounds with column names fit, lwr, and upr if interval is set. If se.fit is TRUE, a list with the following components is returned:

`fit`	vector or matrix as above
`se.fit`	standard error of predicted means
`residual.scale`	residual standard deviations
`df`	degrees of freedom for residual

Author(s)