Note: This page has been translated by MathWorks. Please click here

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

**Class: **LinearMixedModel

Predict response of linear mixed-effects model

`ypred = predict(lme)`

`ypred = predict(lme,tblnew)`

`ypred = predict(lme,Xnew,Znew)`

`ypred = predict(lme,Xnew,Znew,Gnew)`

`ypred = predict(___,Name,Value)`

```
[ypred,ypredCI]
= predict(___)
```

```
[ypred,ypredCI,DF]
= predict(___)
```

returns
a vector of conditional predicted
responses `ypred`

= predict(`lme`

)`ypred`

at the original predictors
used to fit the linear mixed-effects model `lme`

.

returns
a vector of conditional predicted responses `ypred`

= predict(`lme`

,`tblnew`

)`ypred`

from
the fitted linear mixed-effects model `lme`

at the
values in the new table or dataset array `tblnew`

.
Use a table or dataset array for `predict`

if you
use a table or dataset array for fitting the model `lme`

.

If a particular grouping variable in `tblnew`

has
levels that are not in the original data, then the random effects
for that grouping variable do not contribute to the `'Conditional'`

prediction
at observations where the grouping variable has new levels.

returns
a vector of conditional predicted responses `ypred`

= predict(`lme`

,`Xnew`

,`Znew`

)`ypred`

from
the fitted linear mixed-effects model `lme`

at the
values in the new fixed- and random-effects design matrices, `Xnew`

and `Znew`

,
respectively. `Znew`

can also be a cell array of
matrices. In this case, the grouping variable `G`

is `ones(n,1)`

,
where *n* is the number of observations used in the
fit.

Use the matrix format for `predict`

if using
design matrices for fitting the model `lme`

.

returns
a vector of conditional predicted responses `ypred`

= predict(`lme`

,`Xnew`

,`Znew`

,`Gnew`

)`ypred`

from
the fitted linear mixed-effects model `lme`

at the
values in the new fixed- and random-effects design matrices, `Xnew`

and `Znew`

,
respectively, and the grouping variable `Gnew`

.

`Znew`

and `Gnew`

can also
be cell arrays of matrices and grouping variables, respectively.

returns
a vector of predicted responses `ypred`

= predict(___,`Name,Value`

)`ypred`

from the
fitted linear mixed-effects model `lme`

with additional
options specified by one or more `Name,Value`

pair
arguments.

For example, you can specify the confidence level, simultaneous confidence bounds, or contributions from only fixed effects.

Was this topic helpful?