R: Add or Drop Single Terms to/from a Bradley Terry Model

add1.BTm {BradleyTerry2} | R Documentation

## Add or Drop Single Terms to/from a Bradley Terry Model

### Description

Add or drop single terms within the limit specified by the `scope` argument. For models with no random effects, compute an analysis of deviance table, otherwise compute the Wald statistic of the parameters that have been added to or dropped from the model.

### Usage

    ## S3 method for class 'BTm'
    add1(object, scope, scale = 0,
      test = c("none", "Chisq", "F"), x = NULL, ...)

    ## S3 method for class 'BTm'
    drop1(object, scope, scale = 0,
      test = c("none", "Chisq", "F"), ...)

### Arguments

`object` | a fitted object of class inheriting from `"BTm"`.
`scope` | a formula specifying the model including all terms to be considered for adding or dropping.
`scale` | an estimate of the dispersion. Not implemented for models with random effects.
`test` | should a p-value be returned? The F test is only appropriate for models with no random effects for which the dispersion has been estimated. The Chisq test is a likelihood ratio test for models with no random effects, otherwise a Wald test.
`x` | a model matrix containing columns for all terms in the scope. Useful if `add1` is to be called repeatedly. Warning: no checks are done on its validity.
`...` | further arguments passed to `add1.glm`.

### Details

The hierarchy is respected when considering terms to be added or dropped: all main effects contained in a second-order interaction must remain, and so on.

In a scope formula . means ‘what is already there’.

For `drop1`, a missing `scope` is taken to mean that all terms in the model may be considered for dropping.

If `scope` includes player covariates and there are players with missing values over these covariates, then a separate ability will be estimated for these players in all fitted models. Similarly if there are missing values in any contest-level variables in `scope`, the corresponding contests will be omitted from all models.

If `formula` includes random effects, the same random effects structure will apply to all models.

### Value

An object of class `"anova"` summarizing the differences in fit between the models.

### Author(s)

Heather Turner

### See Also

`BTm`, `anova.BTm`

### Examples

    attach(flatlizards)
    result <- rep(1, nrow(contests))
    BTmodel1 <- BTm(result, winner, loser,
                    ~ throat.PC1[..] + throat.PC3[..] + (1|..),
                    data = list(contests, predictors),
                    tol = 1e-4, sigma = 2, trace = TRUE)

    drop1(BTmodel1)

    add1(BTmodel1, ~ . + head.length[..] + SVL[..], test = "Chisq")

    BTmodel2 <- update(BTmodel1, formula = ~ . + head.length[..])

    drop1(BTmodel2, test = "Chisq")

[Package BradleyTerry2 version 1.0-6 Index]