Build a model selection table.
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a fitted model object, a list of such objects, or a
more fitted model objects.
optional, custom rank function (returning an information
criterion) to use instead of the default
logical, stating whether the model objects should be re-fitted if
they are not stored in the
indicates whether and how the component models' coefficients
should be standardized. See the argument's description in
optional additional statistics to include in the result,
provided as functions, function names or a list of such (best if named
or quoted). See
model.sel used with
"model.selection" object will re-fit model
objects, unless they are stored in
object (in attribute
extra is provided, or the requested
beta is different
"beta" attribute, or the new
cannot be applied directly to
logLik objects, or new
are given (unless argument
fit = FALSE).
An object of class
c("model.selection", "data.frame"), being a
data.frame, where each row represents one model and columns contain
useful information about each model: the coefficients, df, log-likelihood, the
value of the information criterion used,
Δ_IC and ‘Akaike
If any arguments differ between the modelling function calls, the
result will include additional columns showing them (except for formulas and
some other arguments).
model.selection.object for its structure.
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Cement$X1 <- cut(Cement$X1, 3) Cement$X2 <- cut(Cement$X2, 2) fm1 <- glm(formula = y ~ X1 + X2 * X3, data = Cement) fm2 <- update(fm1, . ~ . - X1 - X2) fm3 <- update(fm1, . ~ . - X2 - X3) ## ranked with AICc by default (msAICc <- model.sel(fm1, fm2, fm3)) ## ranked with BIC model.sel(fm1, fm2, fm3, rank = AIC, rank.args = alist(k = log(nobs(x)))) # or # model.sel(msAICc, rank = AIC, rank.args = alist(k = log(nobs(x)))) # or # update(msAICc, rank = AIC, rank.args = alist(k = log(nobs(x))))
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