apollo_insertFunc: Modifies function to make it compatible with analytic...
In apollo: Tools for Choice Model Estimation and Application

apollo_insertFunc

R Documentation

Modifies function to make it compatible with analytic gradients

Description

Takes a likelihood function and inserts function () before key elements to allow for analytic gradient calculation

Usage

apollo_insertFunc(f, like = TRUE, randCoeff = FALSE, lcPars = FALSE)

Arguments

`f`	Function. Expressions inside it will be turned into functions. Usually `apollo_probabilities` or `apollo_randCoeff`.
`like`	Logical. Must be TRUE if `f` is `apollo_probabilities`. FALSE otherwise.
`randCoeff`	Logical. Must be TRUE if `f` is `apollo_randCoeff`. FALSE otherwise.
`lcPars`	Logical. Must be TRUE if `f` is `apollo_lcPars`. FALSE otherwise.

Details

It modifies the definition of the following models.

apollo_mnl: Turns all elements inside mnl_settings$V into functions.
apollo_ol: Turns ol_settings$V and all elements inside ol_settings$tau into functions.
apollo_op: Turns op_settings$V and all elements inside op_settings$tau into functions.
apollo_normalDensity: Turns normalDensity_settings$xNormal, normalDensity_settings$mu and normalDensity_settings$sigma into functions.

It can only track a maximum of 3 levels of depth in definitions. For example: V <- list() V[["A"]] <- b1*x1A + b2*x2A V[["B"]] <- b1*x1B + b2*x2B mnl_settings1 <- list(alternatives=c("A", "B"), V = V, choiceVar= Y, avail = 1, componentName="MNL1") P[["MNL1"]] <- apollo_mnl(mnl_settings1, functionality) But it may not be able to deal with the following: VA <- b1*x1A + b2*x2A V <- list() V[["A"]] <- VA V[["B"]] <- b1*x1B + b2*x2B mnl_settings1 <- list(alternatives=c("A", "B"), V = V, choiceVar= Y, avail = 1, componentName="MNL1") P[["MNL1"]] <- apollo_mnl(mnl_settings1, functionality) But that might be enough given how apollo_dVdB works.