# Predict method for averaged models Model-averaged predictions, optionally with standard errors. utf-8 ```r ## S3 method for class 'averaging' predict(object, newdata = NULL, se.fit = FALSE, interval = NULL, type = NA, backtransform = FALSE, full = TRUE, ...) ``` ## Arguments - `object`: an object returned by `model.avg`. - `newdata`: optional `data.frame` in which to look for variables with which to predict. If omitted, the fitted values are used. - `se.fit`: logical, indicates if standard errors should be returned. This has any effect only if the `predict` methods for each of the component models support it. - `interval`: currently not used. - `type`: the type of predictions to return (see documentation for `predict` appropriate for the class of used component models). If omitted, the default type is used. See Details . - `backtransform`: if `TRUE`, the averaged predictions are back-transformed from link scale to response scale. This makes sense provided that all component models use the same `family`, and the prediction from each of the component models is calculated on the link scale (as specified by `type`. For `glm`, use `type = "link"`). See Details . - `full`: if `TRUE`, the full model-averaged coefficients are used (only if `se.fit = FALSE` and the component objects are a result of `lm`). - ``...``: arguments to be passed to respective `predict` method (e.g. `level` for `lme` model). ## Returns 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. ## Details `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 subset-averaged 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`. ## Note 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`). ## Author(s) Kamil Barto ## See Also `model.avg`, and `par.avg` for details of model-averaged parameter calculation. `predict.lme`, `predict.gls` ## Examples ```r # 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) ```

predict.averaging function