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
itisNULLshould 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.