Takes a likelihood function and inserts function () before key elements to allow for analytic gradient calculation
apollo_insertFunc(f, like = TRUE, randCoeff = FALSE, lcPars = FALSE)Function f but with relevant expressions turned into function definitions.
Function. Expressions inside it will be turned into functions. Usually apollo_probabilities or
apollo_randCoeff.
Logical. Must be TRUE if f is apollo_probabilities. FALSE otherwise.
Logical. Must be TRUE if f is apollo_randCoeff. FALSE otherwise.
Logical. Must be TRUE if f is apollo_lcPars. FALSE otherwise.
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.