# predict.MCMCglmm

0th

Percentile

##### 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.

MCMCglmm