predict.BTglmmPQL {BradleyTerry2} | R Documentation |

Obtain predictions and optionally standard errors of those predictions from
a `"BTglmmPQL"`

object.

```
## S3 method for class 'BTglmmPQL'
predict(
object,
newdata = NULL,
newrandom = NULL,
level = ifelse(object$sigma == 0, 0, 1),
type = c("link", "response", "terms"),
se.fit = FALSE,
terms = NULL,
na.action = na.pass,
...
)
```

`object` |
a fitted object of class |

`newdata` |
(optional) a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used. |

`newrandom` |
if |

`level` |
an integer vector giving the level(s) at which predictions are
required. Level zero corresponds to population-level predictions (fixed
effects only), whilst level one corresponds to the individual-level
predictions (full model) which are NA for contests involving individuals not
in the original data. By default |

`type` |
the type of prediction required. The default is on the scale of
the linear predictors; the alternative |

`se.fit` |
logical switch indicating if standard errors are required. |

`terms` |
with |

`na.action` |
function determining what should be done with missing
values in |

`...` |
further arguments passed to or from other methods. |

If `newdata`

is omitted the predictions are based on the data used for
the fit. In that case how cases with missing values in the original fit are
treated is determined by the `na.action`

argument of that fit. If
`na.action = na.omit`

omitted cases will not appear in the residuals,
whereas if `na.action = na.exclude`

they will appear (in predictions
and standard errors), with residual value `NA`

. See also
`napredict`

.

Standard errors for the predictions are approximated assuming the variance of the random effects is known, see Booth and Hobert (1998).

If `se.fit = FALSE`

, a vector or matrix of predictions. If
`se = TRUE`

, a list with components

`fit` |
Predictions |

`se.fit` |
Estimated standard errors |

Heather Turner

Booth, J. G. and Hobert, J. P. (1998). Standard errors of
prediction in Generalized Linear Mixed Models. *Journal of the American
Statistical Association* **93**(441), 262 – 272.

```
seedsModel <- glmmPQL(cbind(r, n - r) ~ seed + extract,
random = diag(nrow(seeds)),
family = binomial,
data = seeds)
pred <- predict(seedsModel, level = 0)
predTerms <- predict(seedsModel, type = "terms")
all.equal(pred, rowSums(predTerms) + attr(predTerms, "constant"))
```

[Package *BradleyTerry2* version 1.1-2 Index]