predict.averaging  R Documentation 
Modelaveraged predictions, optionally with standard errors.
## S3 method for class 'averaging' predict(object, newdata = NULL, se.fit = FALSE, interval = NULL, type = NA, backtransform = FALSE, full = TRUE, ...)
object 
an object returned by 
newdata 
optional 
se.fit 
logical, indicates if standard errors should be returned.
This has any effect only if the 
interval 
currently not used. 
type 
the type of predictions to return (see documentation for

backtransform 
if 
full 
if 
... 
arguments to be passed to respective 
predict
ing is possible only with averaging
objects with
"modelList"
attribute, i.e. those created via model.avg
from a model list, or from model.selection
object with argument fit
= TRUE
(which will recreate the model objects, see model.avg
).
If all the component models are ordinary linear models, the prediction can be
made either with the full averaged coefficients (the argument full =
TRUE
this is the default) or subsetaveraged coefficients. Otherwise the
prediction is obtained by calling predict
on each component model and
weighted averaging the results, which corresponds to the assumption that all
predictors are present in all models, but those not estimated are equal zero
(see ‘Note’ in model.avg
). Predictions from component models
with standard errors are passed to par.avg
and averaged in the same way
as the coefficients are.
Predictions on the response scale from generalized models can be calculated by
averaging predictions of each model on the link scale, followed by inverse
transformation (this is achieved with type = "link"
and
backtransform = TRUE
). This is only possible if all component models use
the same family and link function. Alternatively, predictions from each model on
response scale may be averaged (with type = "response"
and
backtransform = FALSE
). Note that this leads to results differing from
those calculated with the former method. See also
predict.glm
.
If se.fit = FALSE
, a vector of predictions, otherwise a list
with components: fit
containing the predictions, and se.fit
with
the estimated standard errors.
This method relies on availability of the predict
methods for the
component model classes (except when all component models are of class
lm
).
The package MuMIn includes predict
methods for lme
,
and gls
that calculate standard errors of the predictions
(with se.fit = TRUE
). They enhance the original predict methods from
package nlme, and with se.fit = FALSE
they return identical result.
MuMIn's versions are always used in averaged model predictions (so it is
possible to predict with standard errors), but from within global environment
they will be found only if MuMIn is before nlme on the
search list (or directly extracted from namespace as
MuMIn:::predict.lme
).
Kamil Bartoń
model.avg
, and par.avg
for details of modelaveraged
parameter calculation.
predict.lme
, predict.gls
# Example from Burnham and Anderson (2002), page 100: fm1 < lm(y ~ X1 + X2 + X3 + X4, data = Cement) ms1 < dredge(fm1) confset.95p < get.models(ms1, subset = cumsum(weight) <= .95) avgm < model.avg(confset.95p) nseq < function(x, len = length(x)) seq(min(x, na.rm = TRUE), max(x, na.rm=TRUE), length = len) # New predictors: X1 along the range of original data, other # variables held constant at their means newdata < as.data.frame(lapply(lapply(Cement[, 1], mean), rep, 25)) newdata$X1 < nseq(Cement$X1, nrow(newdata)) n < length(confset.95p) # Predictions from each of the models in a set, and with averaged coefficients pred < data.frame( model = sapply(confset.95p, predict, newdata = newdata), averaged.subset = predict(avgm, newdata, full = FALSE), averaged.full = predict(avgm, newdata, full = TRUE) ) opal < palette(c(topo.colors(n), "black", "red", "orange")) matplot(newdata$X1, pred, type = "l", lwd = c(rep(2,n),3,3), lty = 1, xlab = "X1", ylab = "y", col=1:7) # For comparison, prediction obtained by averaging predictions of the component # models pred.se < predict(avgm, newdata, se.fit = TRUE) y < pred.se$fit ci < pred.se$se.fit * 2 matplot(newdata$X1, cbind(y, y  ci, y + ci), add = TRUE, type="l", lty = 2, col = n + 3, lwd = 3) legend("topleft", legend=c(lapply(confset.95p, formula), paste(c("subset", "full"), "averaged"), "averaged predictions + CI"), lty = 1, lwd = c(rep(2,n),3,3,3), cex = .75, col=1:8) palette(opal)
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