| 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 |
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")