Description Usage Arguments Details Value Author(s) See Also Examples

Extract a subset of a model selection table.

1 2 3 4 5 6 |

`x` |
a |

`subset,select` |
logical expressions indicating columns and rows to keep.
See |

`i,j` |
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` |
logical value specyfying whether Δ_IC should be calculated for the new set of models (not done by default). |

`exact` |
logical, see |

`...` |
further arguments passed to |

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 pseudo-function `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`

.

Enclosing in `I`

prevents the name from being interpreted as a column name.

To select rows where one variable can be present conditional on the presence of
other variables, the function `dc`

(**d**ependency **c**hain) 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`

).

A `model.selection`

object containing only the selected models (rows).
If columns are selected (*via* argument `select`

or the second index
`x[, j]`

) and 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 `[<-`

,
`[[<-`

or `$<-`

produces a regular data frame.

Kamil Bartoń

`dredge`

, `subset`

and `[.data.frame`

for
subsetting and extracting from `data.frame`

s.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
fm1 <- lm(formula = y ~ X1 + X2 + X3 + X4, data = Cement, na.action = na.fail)
# generate models where each variable is included only if the previous
# are included too, e.g. X2 only if X1 is there, and X3 only if X2 and X1
dredge(fm1, subset = dc(X1, X2, X3, X4))
# which is equivalent to
# dredge(fm1, subset = (!X2 | X1) & (!X3 | X2) & (!X4 | X3))
# alternatively, generate "all possible" combinations
ms0 <- dredge(fm1)
# ...and afterwards select the subset of models
subset(ms0, dc(X1, X2, X3, X4))
# which is equivalent to
# subset(ms0, (has(!X2) | has(X1)) & (has(!X3) | has(X2)) & (has(!X4) | has(X3)))
# Different ways of finding a confidence set of models:
# delta(AIC) cutoff
subset(ms0, delta <= 4, recalc.weights = FALSE)
# cumulative sum of Akaike weights
subset(ms0, cumsum(weight) <= .95, recalc.weights = FALSE)
# relative likelihood
subset(ms0, (weight / weight[1]) > (1/8), recalc.weights = FALSE)
``` |

```
Fixed term is "(Intercept)"
Global model call: lm(formula = y ~ X1 + X2 + X3 + X4, data = Cement, na.action = na.fail)
---
Model selection table
(Intrc) X1 X2 X3 X4 df logLik AICc delta weight
4 52.58 1.468 0.6623 4 -28.156 69.3 0.00 0.826
8 48.19 1.696 0.6569 0.2500 5 -26.952 72.5 3.16 0.170
16 62.41 1.551 0.5102 0.1019 -0.1441 6 -26.918 79.8 10.52 0.004
2 81.48 1.869 3 -48.206 105.1 35.77 0.000
1 95.42 2 -53.168 111.5 42.22 0.000
Models ranked by AICc(x)
Fixed term is "(Intercept)"
Global model call: lm(formula = y ~ X1 + X2 + X3 + X4, data = Cement, na.action = na.fail)
---
Model selection table
(Intrc) X1 X2 X3 X4 df logLik AICc delta weight
4 52.58 1.468 0.6623 4 -28.156 69.3 0.00 0.826
8 48.19 1.696 0.6569 0.2500 5 -26.952 72.5 3.16 0.170
16 62.41 1.551 0.5102 0.1019 -0.1441 6 -26.918 79.8 10.52 0.004
2 81.48 1.869 3 -48.206 105.1 35.77 0.000
1 95.42 2 -53.168 111.5 42.22 0.000
Models ranked by AICc(x)
Global model call: lm(formula = y ~ X1 + X2 + X3 + X4, data = Cement, na.action = na.fail)
---
Model selection table
(Intrc) X1 X2 X3 X4 df logLik AICc delta weight
4 52.58 1.468 0.6623 4 -28.156 69.3 0.00 0.566
12 71.65 1.452 0.4161 -0.2365 5 -26.933 72.4 3.13 0.119
8 48.19 1.696 0.6569 0.25 5 -26.952 72.5 3.16 0.116
10 103.10 1.440 -0.6140 4 -29.817 72.6 3.32 0.107
14 111.70 1.052 -0.41 -0.6428 5 -27.310 73.2 3.88 0.081
Models ranked by AICc(x)
Global model call: lm(formula = y ~ X1 + X2 + X3 + X4, data = Cement, na.action = na.fail)
---
Model selection table
(Intrc) X1 X2 X3 X4 df logLik AICc delta weight
4 52.58 1.468 0.6623 4 -28.156 69.3 0.00 0.566
12 71.65 1.452 0.4161 -0.2365 5 -26.933 72.4 3.13 0.119
8 48.19 1.696 0.6569 0.25 5 -26.952 72.5 3.16 0.116
10 103.10 1.440 -0.6140 4 -29.817 72.6 3.32 0.107
Models ranked by AICc(x)
Global model call: lm(formula = y ~ X1 + X2 + X3 + X4, data = Cement, na.action = na.fail)
---
Model selection table
(Intrc) X1 X2 X3 X4 df logLik AICc delta weight
4 52.58 1.468 0.6623 4 -28.156 69.3 0.00 0.566
12 71.65 1.452 0.4161 -0.2365 5 -26.933 72.4 3.13 0.119
8 48.19 1.696 0.6569 0.25 5 -26.952 72.5 3.16 0.116
10 103.10 1.440 -0.6140 4 -29.817 72.6 3.32 0.107
14 111.70 1.052 -0.41 -0.6428 5 -27.310 73.2 3.88 0.081
Models ranked by AICc(x)
```

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