predict.lm {stats}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, na.action = na.pass, ...)

Arguments

object Object of class inheriting from "lm"
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.
... 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, sometimes referred to as narrow vs. wide intervals.

If the fit is rank-deficient, some of the columns of the design matrix will have been dropped. Prediction from such a fit only makes sense if newdata is contained in the same subspace as the original data. That cannot be checked accurately, so a warning is issued.

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 predicted means
residual.scale residual standard deviations
df degrees of freedom for residual

Note

Variables are first looked for in newdata and then searched for in the usual way (which will include the environment of the formula used in the fit). As from R 2.0.0 a warning will be given if the variables found are not of the same length as those in newdata if it was supplied.

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")

[Package stats version 2.1.0 Index]