# predict.clm

##### Predict Method for CLM fits

Obtains predictions from a cumulative link model.

- Keywords
- models

##### Usage

```
# S3 method for clm
predict(object, newdata, se.fit = FALSE, interval = FALSE,
level = 0.95,
type = c("prob", "class", "cum.prob", "linear.predictor"),
na.action = na.pass, ...)
```

##### Arguments

- object
a fitted object of class inheriting from

`clm`

.- newdata
optionally, a data frame in which to look for variables with which to predict. Note that all predictor variables should be present having the same names as the variables used to fit the model. If the response variable is present in

`newdata`

predictions are obtained for the levels of the response as given by`newdata`

. If the response variable is omitted from`newdata`

predictions are obtained for all levels of the response variable for each of the rows of`newdata`

.- se.fit
should standard errors of the predictions be provided? Not applicable and ignored when

`type = "class"`

.- interval
should confidence intervals for the predictions be provided? Not applicable and ignored when

`type = "class"`

.- level
the confidence level.

- type
the type of predictions.

`"prob"`

gives probabilities,`"class"`

gives predicted response class membership defined as highest probability prediction,`"cum.prob"`

gives cumulative probabilities (see details) and`"linear.predictor"`

gives predictions on the scale of the linear predictor including the boundary categories.- 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 and `type = "prob"`

a vector of
fitted probabilities are returned identical to the result from
`fitted`

.

If `newdata`

is supplied and the response
variable is omitted, then predictions, standard errors and intervals
are matrices rather than vectors with the same number of rows as
`newdata`

and with one column for each response class. If
`type = "class"`

predictions are always a vector.

If `newdata`

is omitted, the way missing values in the original fit are handled
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, standard
errors or interval limits), with residual value `NA`

. See also
`napredict`

.

If `type = "cum.prob"`

or `type = "linear.predictor"`

there
will be two sets of predictions, standard errors and intervals; one
for j and one for j-1 (in the usual notation) where j = 1, ..., J index
the response classes.

If newdata is supplied and the response variable is omitted, then
`predict.clm`

returns much the same thing as `predict.polr`

(matrices of predictions). Similarly, if `type = "class"`

.

If the fit is rank-deficient, some of the columns of the design matrix
will have been dropped. Prediction from such a fit only makes sense if
newdata is contained in the same subspace as the original data. That
cannot be checked accurately, so a warning is issued
(cf. `predict.lm`

).

If a flexible link function is used (`Aranda-Ordaz`

or `log-gamma`

)
standard errors and confidence intervals of predictions do not take the
uncertainty in the link-parameter into account.

##### Value

A list containing the following components

predictions or fitted values if `newdata`

is not
supplied.

if `se.fit=TRUE`

standard errors of the predictions
otherwise `NULL`

.

if `interval=TRUE`

lower and upper confidence
limits.

##### See Also

##### Examples

```
# NOT RUN {
## simple model:
fm1 <- clm(rating ~ contact + temp, data=wine)
summary(fm1)
## Fitted values with standard errors and confidence intervals:
predict(fm1, se.fit=TRUE, interval=TRUE) # type="prob"
## class predictions for the observations:
predict(fm1, type="class")
newData <- expand.grid(temp = c("cold", "warm"),
contact = c("no", "yes"))
## Predicted probabilities in all five response categories for each of
## the four cases in newData:
predict(fm1, newdata=newData, type="prob")
## now include standard errors and intervals:
predict(fm1, newdata=newData, se.fit=TRUE, interval=TRUE, type="prob")
# }
```

*Documentation reproduced from package ordinal, version 2019.12-10, License: GPL (>= 2)*