MuMIn (version 1.7.7)

predict.averaging: Predict method for the averaged model

Description

Model-averaged predictions with optional standard errors.

Usage

## S3 method for class 'averaging':
predict(object, newdata = NULL, se.fit = FALSE,
    interval = NULL, type = NA, backtransform = FALSE, full = TRUE, ...)

Value

• 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.

encoding

utf-8

Details

If all the component models are oridinary 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.

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 different results. See also predict.glm.

See Also

model.avg See par.avg for details of model-averaged parameter calculation.

Examples

require(graphics)

# Example from Burnham and Anderson (2002), page 100:
data(Cement)
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[1:4], 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)