

   PPrreeddiiccttiinngg ffrroomm LLiinneeaarr MMooddeell FFiittss

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

   DDeessccrriippttiioonn::

        `predict.lm' produces predicted values, obtained by
        evaluating the regression function in the frame `new-
        data'.  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 (tol-
        erance) intervals at the specified `level'.

   VVaalluuee::

        `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 compo-
        nents 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

   SSeeee AAllssoo::

        The model fitting function `lm', `predict'.

   EExxaammpplleess::

        ## 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)

