| 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 |
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 |
na.action |
function determining what should be done with missing
values in |
... |
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.
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 predictions |
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")