MuMIn (version 1.47.5)

dredge: Automated model selection


Generate a model selection table of models with combinations (subsets) of fixed effect terms in the global model, with optional model inclusion rules.


dredge(global.model, beta = c("none", "sd", ""), evaluate = TRUE,
  rank = "AICc", fixed = NULL, m.lim = NULL, m.min, m.max, subset,
  trace = FALSE, varying, extra, ct.args = NULL, deps = attr(allTerms0, "deps"),
  cluster = NULL,

# S3 method for model.selection print(x, abbrev.names = TRUE, warnings = getOption("warn") != -1L, ...)


An object of class c("model.selection", "data.frame"), being a data.frame, where each row represents one model. See model.selection.object for its structure.



a fitted ‘global’ model object. See ‘Details’ for a list of supported types.


indicates whether and how the coefficients are standardized, and must be one of "none", "sd" or "". You can specify just the initial letter. "none" corresponds to unstandardized coefficients, "sd" and "" to coefficients standardized by SD and Partial SD, respectively. For backwards compatibility, logical value is also accepted, TRUE is equivalent to "sd" and FALSE to "none". See std.coef.


whether to evaluate and rank the models. If FALSE, a list of unevaluated calls is returned.


optionally, the rank function returning a sort of an information criterion, to be used instead AICc, e.g. AIC, QAIC or BIC. See ‘Details’.


optional, either a single-sided formula or a character vector giving names of terms to be included in all models. See ‘Subsetting’.

m.lim, m.max, m.min

optionally, the limits c(lower, upper) for the number of terms in a single model (excluding the intercept). An NA means no limit. See ‘Subsetting’. Specifying limits as m.min and m.max is allowed for backward compatibility.


logical expression describing models to keep in the resulting set. See ‘Subsetting’.


if TRUE or 1, all calls to the fitting function are printed before actual fitting takes place. If trace > 1, a progress bar is displayed.


optionally, a named list describing the additional arguments to vary between the generated models. Item names correspond to the arguments, and each item provides a list of choices (i.e. list(arg1 = list(choice1, choice2, ...), ...)). Complex elements in the choice list (such as family objects) should be either named (uniquely) or quoted (unevaluated, e.g. using alist, see quote), otherwise the result may be visually unpleasant. See example in Beetle.


optional additional statistics to be included in the result, provided as functions, function names or a list of such (preferably named or quoted). As with the rank argument, each function must accept as an argument a fitted model object and return (a value coercible to) a numeric vector. This could be, for instance, additional information criteria or goodness-of-fit statistics. The character strings "R^2" and "adjR^2" are treated in a special way and add a likelihood-ratio based R² and modified-R² to the result, respectively (this is more efficient than using r.squaredLR directly).


a model.selection object, returned by dredge.


should printed term names be abbreviated? (useful with complex models).


if TRUE, errors and warnings issued during the model fitting are printed below the table (only with pdredge). To permanently remove the warnings, set the object's attribute "warnings" to NULL.


optional list of arguments to be passed to coefTable (e.g. dispersion parameter for glm affecting standard errors used in subsequent model averaging).


a “dependency matrix” as returned by getAllTerms, attribute "deps". Can be used to fine-tune marginality exceptions.


if a valid "cluster" object is provided it is used for parallel execution of the fitting function. If NULL or omitted single threaded execution is performed.

With parallel calculation, an extra argument check is accepted.

See pdredge for details and examples.


optional arguments for the rank function. Any can be an unevaluated expression, in which case any x within it will be substituted with the current model.


Kamil Bartoń


Models are fitted through repeated evaluation of the modified call extracted from the global.model (in a similar fashion to update). This approach, while having the advantage that it can be applied to most model types through the usual formula interface, can have a considerable computational overhead.

Note that the number of combinations grows exponentially with the number of predictors (\(2^{N}\), less when interactions are present, see below).

The fitted model objects are not stored in the result. To get (a subset of) the models, use get.models on the object returned by dredge. Another way to get all the models is to run lapply(dredge(..., evaluate = FALSE), eval), which avoids fitting models twice.

For a list of model types that can be used as a global.model see the list of supported models. Modelling functions that do not store a call in their result should be evaluated via a wrapper function created by updateable.

Information criterion

rank is found by a call to and may be specified as a function, a symbol, or as a character string specifying a function to be searched for from the environment of the call to dredge. It can be also a one-element named list, where the first element is taken as the rank function. The function rank must accept a model object as its first argument and always return a scalar.


By default, marginality constraints are respected, so “all possible combinations” include only those containing interactions with their respective main effects and all lower-order terms. However, if global.model makes an exception to this principle (e.g. due to a nested design such as a / (b + d)), this will be reflected in the subset models.


There are three ways to constrain the resulting set of models: setting limits to the number of terms in a model with m.lim, binding term(s) to all models using fixed, and the subset argument can be used for more complex rules. For a model to be included in the selection table, its formulation must satisfy all these conditions.

subset may be an expression or a matrix. The latter should be a lower triangular matrix with logical values, where the columns and rows correspond to global.model terms. Value subset["a", "b"] == FALSE will exclude any model containing both a and b terms.
demo(dredge.subset) has examples of using the subset matrix in conjunction with correlation matrices to exclude models containing collinear predictors.

In the form of expression, the argument subset acts in a similar fashion to that in the function subset for data.frames: model terms can be referred to by name as variables in the expression, with the difference being that are interpreted as logical values (i.e. equal to TRUE if the term exists in the model).

The expression can contain any of the global.model term names, as well as names of the varying list items. global.model term names take precedence when identical to names of varying, so to avoid ambiguity varying variables in subset expression should be enclosed in V() (e.g. V(family) == "Gamma") assuming that varying is something like list(family = c("Gamma", ...))).

If item names in varying are missing, the items themselves are coerced to names. Call and symbol elements are represented as character values (via deparse), and anything other than numeric, logical, character and NULL values is replaced by item numbers (e.g. varying = list(family = list(gaussian, Gamma) should be referred to as subset = V(family) == 2. This can quickly become confusing, so it is recommended to use named lists. Examples can be found in demo(dredge.varying).

Term names appearing in fixed and subset must be given exactly as they are returned by getAllTerms(global.model), which may differ from the original term names (e.g. the interaction term components are ordered alphabetically).

The with(x) and with(+x) notation indicates, respectively, any and all interactions including the main effect term x. This is only effective with marginality exceptions. The extended form with(x, order) allows to specify the order of interaction of terms of which x is a part. For instance, with(b, 2:3) selects models with at least one second- or third-order interaction of variable b. The second (positional) argument is coerced to an integer vector. The “dot” notation .(x) is an alias for with.

The special variable `*nvar*` (backtick-quoted), in the subset expression is equal to the number of terms in the model (not the number of estimated parameters).

To make the inclusion of a model term conditional on the presence of another one, the function dc (“dependency chain”) can be used in the subset expression. dc takes any number of term names as arguments, and allows a term to be included only if all preceding ones are also present (e.g. subset = dc(a, b, c) allows for models a, a+b and a+b+c but not b, c, b+c or a+c).

subset expression can have a form of an unevaluated call, expression object, or a one-sided formula. See ‘Examples’.

Compound model terms (such as interactions, ‘as-is’ expressions within I() or smooths in gam) should be enclosed within curly brackets (e.g. {s(x,k=2)}), or backticks (like non-syntactic names, e.g. `s(x, k = 2)` ), except when they are arguments to with or dc. Backticks-quoted names must match exactly (including whitespace) the term names as returned by getAllTerms.

subset expression syntax summary

a & b

indicates that model terms a and b must be present (see Logical Operators)

{log(x,2)} or `log(x, 2)`

represent a complex model term log(x, 2)


represents a varying item x


indicates that at least one term containing the main effect term x must be present


indicates that all the terms containing the main effect term x must be present

with(x, n:m)

indicates that at least one term containing an n-th to m-th order interaction term of x must be present

dc(a, b, c,...)

‘dependency chain’: b is allowed only if a is present, and c only if both a and b are present, etc.


the number of terms in the model.

To simply keep certain terms in all models, use of argument fixed is much more efficient. The fixed formula is interpreted in the same manner as model formula and so the terms must not be quoted.

Missing values

Use of na.action = "na.omit" (R's default) or "na.exclude" in global.model must be avoided, as it results with sub-models fitted to different data sets if there are missing values. An error is thrown if it is detected.

It is a common mistake to give na.action as an argument in the call to dredge (typically resulting in an error from the rank function to which the argument is passed through ‘...’), while the correct way is either to pass na.action in the call to the global model or to set it as a global option.


If present in the global.model, the intercept will be included in all sub-models.


There are subset and plot methods, the latter creates a graphical representation of model weights and per-model term sum of weights. Coefficients can be extracted with coef or coefTable.

See Also

pdredge is a parallelized version of this function (uses a cluster).

get.models, model.avg. model.sel for manual model selection tables.

Possible alternatives: glmulti in package glmulti and bestglm (bestglm). regsubsets in package leaps also performs all-subsets regression.

Variable selection through regularization provided by various packages, e.g. glmnet, lars or glmmLasso.


Run this code
# Example from Burnham and Anderson (2002), page 100:

#  prevent fitting sub-models to different datasets
oop <- 
options(na.action = "")

fm1 <- lm(y ~ ., data = Cement)
dd <- dredge(fm1)
subset(dd, delta < 4)

# Visualize the model selection table:
 if(require(graphics)) { 
par(mar = c(3,5,6,4))
plot(dd, labAsExpr = TRUE)

# Model average models with delta AICc < 4
model.avg(dd, subset = delta < 4)

#or as a 95% confidence set:
model.avg(dd, subset = cumsum(weight) <= .95) # get averaged coefficients

#'Best' model
summary(get.models(dd, 1)[[1]])

if (FALSE) {
# Examples of using 'subset':
# keep only models containing X3
dredge(fm1, subset = ~ X3) # subset as a formula
dredge(fm1, subset = expression(X3)) # subset as expression object
# the same, but more effective:
dredge(fm1, fixed = "X3")
# exclude models containing both X1 and X2 at the same time
dredge(fm1, subset = !(X1 && X2))
# Fit only models containing either X3 or X4 (but not both);
# include X3 only if X2 is present, and X2 only if X1 is present.
dredge(fm1, subset = dc(X1, X2, X3) && xor(X3, X4))
# the same as above, without "dc"
dredge(fm1, subset = (X1 | !X2) && (X2 | !X3) && xor(X3, X4))

# Include only models with up to 2 terms (and intercept)
dredge(fm1, m.lim = c(0, 2))

# Add R^2 and F-statistics, use the 'extra' argument
dredge(fm1, m.lim = c(NA, 1), extra = c("R^2", F = function(x)

# with summary statistics:
dredge(fm1, m.lim = c(NA, 1), extra = list(
    "R^2", "*" = function(x) {
        s <- summary(x)
        c(Rsq = s$r.squared, adjRsq = s$adj.r.squared,
            F = s$fstatistic[[1]])

# Add other information criteria (but rank with AICc):
dredge(fm1, m.lim = c(NA, 1), extra = alist(AIC, BIC, ICOMP, Cp))

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