predict.glm {stats} | R Documentation |

Obtains predictions and optionally estimates standard errors of those predictions from a fitted generalized linear model object.

## S3 method for class 'glm': predict(object, newdata = NULL, type = c("link", "response", "terms"), se.fit = FALSE, dispersion = NULL, terms = NULL, na.action = na.pass, ...)

`object` |
a fitted object of class inheriting from `"glm"` . |

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

`type` |
the type of prediction required. The default is on the
scale of the linear predictors; the alternative `"response"`
is on the scale of the response variable. Thus for a default
binomial model the default predictions are of log-odds (probabilities
on logit scale) and `type = "response"` gives the predicted
probabilities. The `"terms"` option returns a matrix giving the
fitted values of each term in the model formula on the linear predictor
scale.
The value of this argument can be abbreviated. |

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

`dispersion` |
the dispersion of the GLM fit to be assumed in
computing the standard errors. If omitted, that returned by
`summary` applied to the object is used. |

`terms` |
with `type="terms"` by default all terms are returned.
A character vector specifies which terms are to be returned |

`na.action` |
function determining what should be done with missing
values in `newdata` . The default is to predict `NA` . |

`...` |
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 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`

.

If `se = FALSE`

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

, a list with components

`fit` |
Predictions |

`se.fit` |
Estimated standard errors |

`residual.scale` |
A scalar giving the square root of the dispersion used in computing the standard errors. |

Variables are first looked for in `newdata`

and then searched for
in the usual way (which will include the environment of the formula
used in the fit). A warning will be given if the
variables found are not of the same length as those in `newdata`

if it was supplied.

## example from Venables and Ripley (2002, pp. 190-2.) ldose <- rep(0:5, 2) numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16) sex <- factor(rep(c("M", "F"), c(6, 6))) SF <- cbind(numdead, numalive=20-numdead) budworm.lg <- glm(SF ~ sex*ldose, family=binomial) summary(budworm.lg) plot(c(1,32), c(0,1), type = "n", xlab = "dose", ylab = "prob", log = "x") text(2^ldose, numdead/20, as.character(sex)) ld <- seq(0, 5, 0.1) lines(2^ld, predict(budworm.lg, data.frame(ldose=ld, sex=factor(rep("M", length(ld)), levels=levels(sex))), type = "response")) lines(2^ld, predict(budworm.lg, data.frame(ldose=ld, sex=factor(rep("F", length(ld)), levels=levels(sex))), type = "response"))

[Package *stats* version 2.5.0 Index]