MCMCglmm: predict.MCMCglmm – R documentation

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MCMCglmm

predict.MCMCglmm

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; 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

Author(s)

Jarrod Hadfield j.hadfield@ed.ac.uk

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.