\Header{predict.lm}{Predict method for Linear Model Fits}
\alias{predict.mlm}{predict.lm}
\keyword{regression}{predict.lm}
\begin{Description}\relax
Predicted values based on linear model object
\end{Description}
\begin{Usage}
\begin{verbatim}
## 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, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] Object of class inheriting from \code{"lm"}
\item[\code{newdata}] Data frame in which to predict
\item[\code{se.fit}] A switch indicating if standard errors are required.
\item[\code{scale}] Scale parameter for std.err. calculation
\item[\code{df}] Degrees of freedom for scale
\item[\code{interval}] Type of interval calculation
\item[\code{level}] Tolerance/confidence level
\item[\code{type}] Type of prediction (response or model term)
\item[\code{terms}] If \code{type="terms"}, which terms (default is all terms)
\item[\code{na.action}] function determining what should be done with missing
values in \code{newdata}.  The default is to predict \code{NA}.
\item[\code{...}] further arguments passed to or from other methods.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\code{predict.lm} produces predicted values, obtained by evaluating
the regression function in the frame \code{newdata} (which defaults to
\code{model.frame(object)}.  If the logical \code{se.fit} is
\code{TRUE}, standard errors of the predictions are calculated.  If
the numeric argument \code{scale} is set (with optional \code{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 \code{intervals} specifies computation of confidence or
prediction (tolerance) intervals at the specified \code{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 \code{newdata} is contained in the same subspace as the original
data. That cannot be checked accurately, so a warning is issued.
\end{Details}
\begin{Value}
\code{predict.lm} produces a vector of predictions or a matrix of
predictions and bounds with column names \code{fit}, \code{lwr}, and
\code{upr} if \code{interval} is set.  If \code{se.fit} is
\code{TRUE}, a list with the following components is returned: 
\begin{ldescription}
\item[\code{fit}] vector or matrix as above
\item[\code{se.fit}] standard error of predictions
\item[\code{residual.scale}] residual standard deviations
\item[\code{df}] degrees of freedom for residual
\end{ldescription}
\end{Value}
\begin{Note}\relax
Offsets specified by \code{offset} in the fit by \code{\Link{lm}}
will not be included in predictions, whereas those specified by an
offset term in the formula will be.
\end{Note}
\begin{SeeAlso}\relax
The model fitting function \code{\Link{lm}}, \code{\Link{predict}},
\code{\Link{SafePrediction}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
## 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")
\end{ExampleCode}
\end{Examples}

