predict.MCMCglmm: Predict method for GLMMs fitted with MCMCglmm

In MCMCglmm: MCMC Generalised Linear Mixed Models

Predict method for GLMMs fitted with MCMCglmm

Description

Predicted values for GLMMs fitted with MCMCglmm

Usage

## S3 method for class '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; 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

Author(s)

Jarrod Hadfield [e-mail]

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

MCMCglmm

MCMCglmm documentation built on July 9, 2023, 5:24 p.m.