# predict.MCMCglmm

##### Predict method for GLMMs fitted with MCMCglmm

Predicted values for GLMMs fitted with MCMCglmm

- Keywords
- models

##### Usage

```
# S3 method for MCMCglmm
predict(object, newdata=NULL, marginal=object$Random$formula,
type="response", interval="none", level=0.95, it=NULL,
posterior="all", verbose=FALSE, approx="numerical", …)
```

##### Arguments

- object
an object of class

`"MCMCglmm"`

- newdata
An optional data frame in which to look for variables with which to predict

- marginal
formula defining random effects to be maginalised

- type
character; either "terms" (link scale) or "response" (data scale)

- interval
character; either "none", "confidence" or "prediction"

- level
A numeric scalar in the interval (0,1) giving the target probability content of the intervals.

- it
integer; optional, MCMC iteration on which predictions should be based

- posterior
character; if

`it`

is`NULL`

should marginal posterior predictions be calculated ("all"), or should they be made conditional on the marginal posterior means ("mean") of the parameters, the posterior modes ("mode"), or a random draw from the posterior ("distribution").- verbose
logical; if

`TRUE`

, warnings are issued with newdata when the original model has fixed effects that do not appear in newdata and/or newdata has random effects not present in the original model.- approx
character; for distributions for which the mean cannot be calculated analytically what approximation should be used: numerical integration (

`numerical`

; slow), second order Taylor expansion (`taylor2`

) and for logistic models approximations presented in Diggle (2004) (`diggle`

) and McCulloch and Searle (2001) (`mcculloch`

)- …
Further arguments to be passed

##### Value

Expectation and credible interval

##### References

Diggle P, et al. (2004). Analysis of Longitudinal Data. 2nd Edition. Oxford University Press.

McCulloch CE and Searle SR (2001). Generalized, Linear and Mixed Models. John Wiley & Sons, New York.

##### See Also

*Documentation reproduced from package MCMCglmm, version 2.30, License: GPL (>= 2)*