predict.manylm is a function for predictions from the
result of the model fitting function manylm.
# S3 method for manylm
predict(object, newdata=NULL, se.fit = FALSE,
type = c("response", "terms"), terms = NULL, na.action = na.pass, ...)object of class inheriting from manylm.
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.
type of prediction (response or model term), Possible values: "response", "terms".
if type="terms", which terms (default is all terms).
function determining what should be done with missing values in
newdata. The default is to predict NA.
further arguments passed to or from other methods.
predict.manylm produces a matrix of predictions or if interval
is set an array of predictions and bounds, where the first dimension has the names:
fit, lwr, and upr.
If se.fit is TRUE, a list with the following components is returned:
vector or matrix as above
a matrix with the standard errors of the predicted means
vector or matrix as a vector of residual standard deviations
numeric, the degrees of freedom for residual
predict.manylm 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, sometimes
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
if 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 is determined by the na.action argument of that
fit. If na.action = na.omit omitted cases will not appear in
the residuals, whereas if na.action = na.exclude they will
appear (in predictions, standard errors or interval limits),
with residual value 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
variance(s) pred.var. This can be a multiple of res.var,
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.
# NOT RUN {
data(spider)
spiddat <- mvabund(spider$abund[1:20, ])
dat = spider$x[1:20,]
manylm.fit <- manylm(spiddat~soil.dry+bare.sand, data=dat)
predict(manylm.fit)
predict(manylm.fit, se.fit = TRUE)
new <- spider$x[21:28,]
predict(manylm.fit, new, se.fit = TRUE)
# }
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