An MCPL model, containing a learning and response model.

`learningModel`

:A

`LearningModel-class`

object.`responseModel`

:A

`ResponseModel-class`

object.

- AIC
`signature(object = "McplModel")`

: Akaike Information Criterion- based on response model.- AICc
`signature(object = "McplModel")`

: Corrected Akaike Information Criterion - based on response model.- BIC
`signature(object = "McplModel")`

: Bayesian Information Criterion/Schwartz Information Criterion - based on response model.- fit
`signature(object = "McplModel")`

: Estimate model parameters (by Maximum Likelihood).- runm
`signature(object = "McplModel")`

: Calculate (trial dependent) model states given current parameters.- getPars
`signature(object = "McplModel")`

: Get current parameter values.- logLik
`signature(object = "McplModel")`

: Log-likelihood for current parameter values - based on response model.- RSquare
`signature(object = "McplModel")`

: Squared correlation coefficient - a.k.a "proportion variance explained" - based on response model.- setPars
`signature(object = "McplModel")`

: Set parameter values.- show
`signature(object = "McplModel")`

: Display object briefly.- summary
`signature(object = "McplModel")`

: Generate object summary.

Maarten Speekenbrink

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.