predict.lm {base}R Documentation

Predict method for Linear Model Fits

Description

Predicted values based on linear model object

Usage

predict[.lm](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 model term prediction, which term

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.

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")
[Package Contents]