dredge  R Documentation 
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", "partial.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 class 'model.selection' print(x, abbrev.names = TRUE, warnings = getOption("warn") != 1L, ...)
global.model 
a fitted ‘global’ model object. See ‘Details’ for a list of supported types. 
beta 
indicates whether and how the coefficients are standardized, and
must be one of 
evaluate 
whether to evaluate and rank the models. If 
rank 
optionally, the rank function returning a sort of an information
criterion, to be used instead 
fixed 
optional, either a singlesided 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 
subset 
logical expression describing models to keep in the resulting set. See ‘Subsetting’. 
trace 
if 
varying 
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. 
extra 
optional additional statistics to include in the result,
provided as functions, function names or a list of such (best if named
or quoted). Similarly as in 
x 
a 
abbrev.names 
should printed term names be abbreviated? (useful with complex models). 
warnings 
if 
ct.args 
optional list of arguments to be passed to

deps 
a “dependency matrix” as returned by 
cluster 
if a valid With parallel calculation, an extra argument See 
... 
optional arguments for the 
Models are fitted through repeated evaluation of modified call extracted from
the global.model
(in a similar fashion as with update
). This
approach, while robust in that it can be applied to most model types through the
usual formula interface, may have considerable computational overhead.
Note that the number of combinations grows exponentially with the number of predictors (2ⁿ, 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
.
rank
is found by a call to match.fun
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
oneelement 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 lowerorder 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
thirdorder 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*`
(backtickquoted), 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 onesided formula
. See ‘Examples’.
Compound model terms (such as interactions, ‘asis’ expressions within
I()
or smooths in gam
) should be enclosed within curly brackets
(e.g. {s(x,k=2)}
), or backticks (like nonsyntactic
names, e.g.
`s(x, k = 2)`
), except when they are arguments to with
or dc
.
Backticksquoted names must match exactly (including whitespace) the term names
as returned by getAllTerms
.
subset
expression syntax summarya & 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)
V(x)
represents a varying
item x
with(x)
indicates that at least one term containing the main effect term x must be present
with(+x)
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 nth to mth 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.
`*nvar*`
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.
Use of na.action = "na.omit"
(R's default) or "na.exclude"
in
global.model
must be avoided, as it results with submodels 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
submodels.
There are subset
and
plot
methods, the latter creates a
graphical representation of model weights and permodel term sum of weights.
Coefficients can be extracted with coef
or coefTable
.
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.
Users should keep in mind the hazards that a “thoughtless approach” of evaluating all possible models poses. Although this procedure is in certain cases useful and justified, it may result in selecting a spurious “best” model, due to the model selection bias.
“Let the computer find out” is a poor strategy and usually reflects the fact that the researcher did not bother to think clearly about the problem of interest and its scientific setting (Burnham and Anderson, 2002).
Kamil Bartoń
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 allsubsets
regression.
Variable selection through regularization provided by various packages, e.g. glmnet, lars or glmmLasso.
# Example from Burnham and Anderson (2002), page 100: # prevent fitting submodels to different datasets options(na.action = "na.fail") fm1 < lm(y ~ ., data = Cement) dd < dredge(fm1) subset(dd, delta < 4) # Visualize the model selection table: 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]]) ## Not run: # 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)) ## End(Not run) # Add R^2 and Fstatistics, use the 'extra' argument dredge(fm1, m.lim = c(NA, 1), extra = c("R^2", F = function(x) summary(x)$fstatistic[[1]])) # with summary statistics: dredge(fm1, m.lim = c(NA, 1), extra = list( "R^2", "*" = function(x) { s < summary(x) c(Rsq = s$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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