Predicting from Linear Model Fits

Usage

predict[.lm](object, newdata = model.frame(object), se.fit = FALSE,  
             scale = NULL, df = Inf, 
             interval = c("none", "confidence", "prediction"), level = 0.95)

Description

predict.lm produces predicted values, obtained by evaluating the regression function in the frame newdata. 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

See Also

The model fitting function lm, predict.

Examples

## Predictions
x <- rnorm(15)
y <- x + rnorm(15)
predict(lm(y ~ x))
predict(lm(y ~ x), data.frame(x = seq(-3, 3, 0.1)), se = TRUE)


[Package Contents]