MuMIn (version 1.3.6)

QAIC: Quasi AIC(c))

Description

Calculates quasi AIC (or quasi AICc) for one or several fitted model objects. This function is provided mainly as an example of custom rank function for use with model.avg and dredge

Usage

QAIC(object, ..., chat)
QAICc(object, ..., chat)

Arguments

object

a fitted model object.

...

optionally more fitted model objects.

chat

c - hat

Value

• If just one object is provided, returns a numeric value with the corresponding QAIC; if more than one object are provided, returns a data.frame with rows corresponding to the objects.

encoding

utf-8

See Also

AICc quasi family used for models with over-dispersion. AIC and BIC may also be used as a custom rank function in dredge and model.avg.

html{

[echo=TRUE,results=rd,stage=render]{ paste( "\\sQuote{\\href{", if(system.file(package="bbmle")!="") "../../bbmle/doc/" else "http://cran.r-project.org/web/packages/bbmle/vignettes/", "quasi.pdf}", "{Dealing with quasi- models in R}}", ", a vignette in the \\pkg{bbmle} package.", sep="") }{

http://cran.r-project.org/web/packages/bbmle/vignettes/quasi.pdf{Dealing with quasi- models in R}, a vignette in the bbmle package.

}

}

[object Object]

# Based on "example(predict.glm)" require(graphics)

budworm <- data.frame( ldose = rep(0:5, 2), numdead = c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16), sex = factor(rep(c("M", "F"), c(6, 6)))) budworm$SF = cbind( numdead = budworm$numdead, numalive = 20 - budworm$numdead)

budworm.lg <- glm(SF ~ sex*ldose, data = budworm, family = quasibinomial)

dd <- dredge(budworm.lg, rank = "QAIC", chat = summary(budworm.lg)$dispersion) # Average all models budworm.avg <- model.avg(get.models(dd, seq(nrow(dd))), method="NA") #model.avg(mod[[1]], mod[[2]], rank = "QAIC", rank.args = list(chat = 1))

plot(c(1,32), c(0,1), type = "n", xlab = "dose", ylab = "prob", log = "x") text(2^budworm$ldose, budworm$numdead/20, as.character(budworm$sex)) ld <- seq(0, 5, 0.1)

newdata <- data.frame(ldose=ld, sex=factor(rep("M", length(ld)), levels=levels(budworm$sex)))

# Predictions from global model / Males pred.lg <- predict(budworm.lg, newdata, se.fit=TRUE, type="response") matplot(2^ld, cbind(pred.lg$fit, pred.lg$fit - (2 * pred.lg$se.fit), pred.lg$fit + (2 * pred.lg$se.fit)), add=TRUE, type="l", col=1)

# Predictions from averaged model / Males pred.avg <- predict(budworm.avg, newdata, se.fit=TRUE, type="response") matplot(2^ld, cbind(pred.avg$fit, pred.avg$fit - (2 * pred.avg$se.fit), pred.avg$fit + (2 * pred.avg$se.fit)), add=TRUE, type="l", col=2)

newdata$sex[] <- "F"

# Predictions from global model / Females pred.lg <- predict(budworm.lg, newdata, se.fit=TRUE, type="response") matplot(2^ld, cbind(pred.lg$fit, pred.lg$fit - (2 * pred.lg$se.fit), pred.lg$fit + (2 * pred.lg$se.fit)), add=TRUE, type="l", col=1)

# Predictions from averaged model / Females pred.avg <- predict(budworm.avg, newdata, se.fit=TRUE, type="response") matplot(2^ld, cbind(pred.avg$fit, pred.avg$fit - (2 * pred.avg$se.fit), pred.avg$fit + (2 * pred.avg$se.fit)), add=TRUE, type="l", col=2)

legend("bottomright", legend=c("full", "averaged"), title="Model", col=1:2, lty=1)

models