predict.lm {base}

R Documentation

Predict method for Linear Model Fits

Description

Predicted values based on linear model object

Usage

## S3 method for class 'lm'
predict(object, newdata, se.fit = FALSE, scale = NULL, df = Inf, 
        interval = c("none", "confidence", "prediction"),
        level = 0.95, type = c("response", "terms"),
        terms = NULL, ...)

Arguments

object	Object of class inheriting from "lm"
newdata	Data frame in which to predict
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)
...	further arguments passed to or from other methods.

Details

predict.lm produces predicted values, obtained by evaluating the regression function in the frame newdata (which defaults to model.frame(object). If the logical se.fit is TRUE, standard errors of the predictions are calculated. If the numeric argument scale is set (with optional df), it is used as the residual standard deviation in the computation of the standard errors, otherwise this is extracted from the model fit. Setting intervals specifies computation of confidence or prediction (tolerance) intervals at the specified level.

Value

predict.lm 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 predictions
residual.scale	residual standard deviations
df	degrees of freedom for residual

Note

Offsets specified by offset in the fit by lm will not be included in predictions, whereas those specified by an offset term in the formula will be.

See Also

The model fitting function lm, predict, SafePrediction

Examples

## Predictions
x <- rnorm(15)
y <- x + rnorm(15)
predict(lm(y ~ x))
new <- data.frame(x = seq(-3, 3, 0.5))
predict(lm(y ~ x), new, se.fit = TRUE)
pred.w.plim <- predict(lm(y ~ x), new, interval="prediction")
pred.w.clim <- predict(lm(y ~ x), new, interval="confidence")
matplot(new$x,cbind(pred.w.clim, pred.w.plim[,-1]),
        lty=c(1,2,2,3,3), type="l", ylab="predicted y")

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