dredge: Evaluate "all possible" models

Description

Runs models with all possible combinations of the explanatory variables in the supplied model.

Usage

dredge(global.model, beta = FALSE, eval = TRUE, rank = "AICc",
	fixed = NULL, m.max = NA, subset, marg.ex = NULL, trace = FALSE,
	...)
## S3 method for class 'model.selection':
print(x, abbrev.names = TRUE, ...)

Arguments

global.model

a fitted global model object. Currently, it can be a lm, glm, rlm, multinom, gam, gls, lme, lmer, sarlm or spauto

beta

logical should standardized coefficients be returned rather than normal ones?

eval

whether to evaluate and rank the models. If FALSE, a list of all possible model formulas is returned

rank

optional custom rank function (information criterion) to be used instead AICc, e.g. QAIC or BIC, See Details

fixed

optional, either a single sided formula or a character vector giving names of terms to be included in all models

m.max

optional, maximum number of terms to be included in single model, defaults to the number of terms in global.model

subset

logical expression to put additional constraints for the set of models. Can contain any of the global.model terms. Run getAllTerms(global.model) to list all the terms. Complex expressions (e.g smooth functions in

marg.ex

a character vector specyfying names of variables for which NOT to check for marginality restrictions when generating model formulas. If this argument is set to TRUE, all model formulas are used (i.e. no checking). See Details.

trace

if TRUE, all calls to the fitting function (i.e. updated global.model calls) are printed.

a model.selection object, returned by dredge.

abbrev.names

Should variable names be abbreviated when printing? (useful with many variables)

...

optional arguments for the rank function. Any can be an expression (of mode call), in which case any x within it will be substituted with a current model.

Value

dredge returns an object of class model.selection, being a data.frame with models' coefficients, k, deviance/RSS, R-squared, AIC, AICc, delta and weight. This depends on a type of model. Models are ordered according to AICc (lowest on top), or by rank function if specified. The attribute "formulas" is a list containing model formulas.

encoding

utf-8

Details

Models are run one by one by calling update with modified formula argument. This method, while robust in that it can be applied to a variety of different models, is not very efficient, so may be time (and memory) consuming.

Handling interactions, dredge respects marginality constraints, so all possible combinations do not include models containing interactions without their respective main effects. This behaviour can be altered by marg.ex argument. It can be used to allow for simple nested designs. For example, with global model of form a / (x + z), use marg.ex = "a" and fixed = "a".

rank is found by a call to match.fun and typically is specified as a function or a symbol (e.g. a backquoted name) or a character string specifying a function to be searched for from the environment of the call to lapply.

Function rank must be able to accept model as a first argument and must always return a scalar.

Examples

Run this code

# Example from Burnham and Anderson (2002), page 100:
data(Cement)
lm1 <- lm(y ~ ., data = Cement)
dd <- dredge(lm1)
subset(dd, delta < 4)

#models with delta.aicc < 4
model.avg(get.models(dd, subset = delta < 4)) # get averaged coefficients

#or as a 95\% confidence set:
top.models <- get.models(dd, cumsum(weight) <= .95)

model.avg(top.models) # get averaged coefficients

#topmost model:
top.models[[1]]

# Examples of using 'subset':
# exclude models containing both X1 and X2
dredge(lm1, subset = !(X1 & X2))
# keep only models containing X3
dredge(lm1, subset = X3)
# the same, but more effective:
dredge(lm1, fixed = "X3")

#Reduce the number of combinations, by including models with up to 2 terms
# (and intercept)
dredge(lm1, m.max = 2)

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