Predict method for Linear Model Fits
Predicted values based on linear model object.
# S3 method for 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, pred.var = res.var/weights, weights = 1, …)
- Object of class inheriting from
- An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
- A switch indicating if standard errors are required.
- Scale parameter for std.err. calculation.
- Degrees of freedom for scale.
- Type of interval calculation. Can be abbreviated.
- Tolerance/confidence level.
- Type of prediction (response or model term). Can be abbreviated.
type = "terms", which terms (default is all terms), a
- function determining what should be done with missing
newdata. The default is to predict
- the variance(s) for future observations to be assumed for prediction intervals. See ‘Details’.
- variance weights for prediction. This can be a numeric
vector or a one-sided model formula. In the latter case, it is
interpreted as an expression evaluated in
- further arguments passed to or from other methods.
predict.lm produces predicted values, obtained by evaluating
the regression function in the frame
newdata (which defaults to
model.frame(object). If the logical
TRUE, standard errors of the predictions are calculated. If
the numeric argument
scale is set (with optional
is used as the residual standard deviation in the computation of the
standard errors, otherwise this is extracted from the model fit.
intervals specifies computation of confidence or
prediction (tolerance) intervals at the specified
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
newdata is contained in the same subspace as the original
data. That cannot be checked accurately, so a warning is issued. If
newdata is omitted the predictions are based on the data
used for the fit. In that case how cases with missing values in the
original fit are handled is determined by the
na.action argument of that
na.action = na.omit omitted cases will not appear in
the predictions, whereas if
na.action = na.exclude they will
appear (in predictions, standard errors or interval limits),
NA. See also
napredict. The prediction intervals are for a single observation at each case in
newdata (or by default, the data used for the fit) with error
pred.var. This can be a multiple of
the estimated value of \(\sigma^2\): the default is to assume that
future observations have the same error variance as those
used for fitting. If
weights is supplied, the inverse of this
is used as a scale factor. For a weighted fit, if the prediction
is for the original data frame,
weights defaults to the weights
used for the model fit, with a warning since it might not be the
intended result. If the fit was weighted and
newdata is given, the
default is to assume constant prediction variance, with a warning.
predict.lm produces a vector of predictions or a matrix of
predictions and bounds with column names
interval is set. For
type = "terms" this
is a matrix with a column per term and may have an attribute
TRUE, a list with the following components is returned:
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). A warning will be given if the
variables found are not of the same length as those in
if it was supplied. Notice that prediction variances and prediction intervals always refer
to future observations, possibly corresponding to the same
predictors as used for the fit. The variance of the residuals
will be smaller. Strictly speaking, the formula used for prediction limits assumes that
the degrees of freedom for the fit are the same as those for the
residual variance. This may not be the case if
not obtained from the fit.
require(graphics) ## 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$x, cbind(pred.w.clim, pred.w.plim[,-1]), lty = c(1,2,2,3,3), type = "l", ylab = "predicted y") ## Prediction intervals, special cases ## The first three of these throw warnings w <- 1 + x^2 fit <- lm(y ~ x) wfit <- lm(y ~ x, weights = w) predict(fit, interval = "prediction") predict(wfit, interval = "prediction") predict(wfit, new, interval = "prediction") predict(wfit, new, interval = "prediction", weights = (new$x)^2) predict(wfit, new, interval = "prediction", weights = ~x^2) ##-- From aov(.) example ---- predict(.. terms) npk.aov <- aov(yield ~ block + N*P*K, npk) (termL <- attr(terms(npk.aov), "term.labels")) (pt <- predict(npk.aov, type = "terms")) pt. <- predict(npk.aov, type = "terms", terms = termL[1:4]) stopifnot(all.equal(pt[,1:4], pt., tolerance = 1e-12, check.attributes = FALSE))