# predict.glm

##### Predict Method for GLM Fits

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

- Keywords
- models, regression

##### Usage

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

##### Arguments

- 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.

##### Details

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`

.

##### Value

If `se.fit = FALSE`

, a vector or matrix of predictions.
For `type = "terms"`

this is a matrix with a column per term, and
may have an attribute `"constant"`

.

If `se.fit = TRUE`

, a list with components

Predictions, as for `se.fit = FALSE`

.

Estimated standard errors.

A scalar giving the square root of the dispersion used in computing the standard errors.

##### Note

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.

##### See Also

##### Examples

`library(stats)`

```
# NOT RUN {
require(graphics)
## 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"))
# }
```

*Documentation reproduced from package stats, version 3.6.1, License: Part of R 3.6.1*