Get fitted / predicted values
Wrapper to get the fitted / predicted response of model at the response scale
getFitted(object, ...) ## Default S3 method: getFitted(object, ...) ## Default S3 method: getResiduals(object, ...) ## S3 method for class 'gam' getFitted(object, ...) ## S3 method for class 'HLfit' getFitted(object, ...) ## S3 method for class 'MixMod' getFitted(object, ...)
object |
a fitted model |
... |
additional parameters to be passed on, usually to the simulate function of the respective model class |
The purpose of this wrapper is to standardize extract the fitted values, which is implemented via predict(model, type = "response") for most model classes.
If you implement this function for a new model class, you should include an option to modifying which REs are included in the predictions. If this option is not available, it is essential that predictions are provided marginally / unconditionally, i.e. without the random effect estimates (because of https://github.com/florianhartig/DHARMa/issues/43), which corresponds to re-form = ~0 in lme4
Florian Hartig
testData = createData(sampleSize = 400, family = gaussian()) fittedModel <- lm(observedResponse ~ Environment1 , data = testData) # response that was used to fit the model getObservedResponse(fittedModel) # predictions of the model for these points getFitted(fittedModel) # extract simulations from the model as matrix getSimulations(fittedModel, nsim = 2) # extract simulations from the model for refit (often requires different structure) x = getSimulations(fittedModel, nsim = 2, type = "refit") getRefit(fittedModel, x[[1]]) getRefit(fittedModel, getObservedResponse(fittedModel))
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