Package ‘MuMIn’

subset.model.selection 55
## S3 method for class model.selection
x[[..., exact = TRUE]]
Arguments
x
subset,select
i,j
a model.selection object to be subsetted.
logical expressions indicating columns and rows to keep. See subset.
indices specifying elements to extract.
recalc.weights logical value specyfying whether Akaike weights should be normalized across
the new set of models to sum to one.
recalc.delta
exact logical, see [.
logical value specyfying whether ∆ IC should be calculated for the new set of
models (not done by default).
... further arguments passed to [.data.frame (drop).
Details
Unlike the method for data.frame, single bracket extraction with only one index x[i] selects rows
(models) rather than columns.
To select rows according to presence or absence of the variables (rather than their value), a pseudofunction
has may be used with subset, e.g. subset(x, has(a, !b)) will select rows with a and
without b (this is equivalent to !is.na(a) & is.na(b)). has can take any number of arguments.
Complex model terms need to be enclosed within curly brackets (e.g {s(a,k=2)}), except for
within has. Backticks-quoting is also possible, but then the name must match exactly (including
whitespace) the term name as returned by getAllTerms.
To select rows where one variable can be present conditional on the presence of other variable(s),
the function dc (dependency chain) can be used. dc takes any number of variables as arguments,
and allows a variable to be included only if all the preceding arguments are also included (e.g.
subset = dc(a, b, c) allows for models of form a, a+b and a+b+c but not b, c, b+c or a+c).
Value
A model.selection object containing only the selected models (rows). If columns are selected
(via argument select or second index x[, j]) and not all if not all essential columns (i.e. all
except "varying" and "extra") are present in the result, a plain data.frame is returned. Similarly,
modifying values in the essential columns with [