predict.lmrob {robustbase} | R Documentation |
Predicted values based on robust linear model object.
## S3 method for class 'lmrob' predict(object, newdata, se.fit = FALSE, scale = NULL, df = NULL, 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 |
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 |
pred.var |
the variance(s) for future observations to be assumed for prediction intervals. See ‘Details’. |
weights |
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.lmrob
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 predicted means |
residual.scale |
residual standard deviations |
df |
degrees of freedom for residual |
Andreas Ruckstuhl
lmrob
and the (non-robust) traditional
predict.lm
method.