apollo_mdcevInside {apollo} | R Documentation |
Calculates the likelihood of a Multiple Discrete Continuous Extreme Value (MDCEV) model without an outside good.
apollo_mdcevInside(V, alternatives, alpha, gamma, sigma, cost, avail, continuousChoice, budget, functionality, minConsumption = NA, rows = "all")
V |
Named list. Utilities of the alternatives. Names of elements must match those in argument 'alternatives'. |
alternatives |
Character vector. Names of alternatives, elements must match the names in list 'V'. |
alpha |
Named list. Alpha parameters for each alternative. As many elements as alternatives. |
gamma |
Named list. Gamma parameters for each alternative. As many elements as alternatives. |
sigma |
Numeric scalar. Scale parameter of the model extreme value type I error. |
cost |
Named list of numeric vectors. Price of each alternative. One element per alternative, each one as long as the number of observations or a scalar. Names must match those in |
avail |
Named list. Availabilities of alternatives, one element per alternative. Names of elements must match those in argument 'alternatives'. Value for each element can be 1 (scalar if always available) or a vector with values 0 or 1 for each observation. If all alternatives are always available, then user can just omit this argument. |
continuousChoice |
Named list of numeric vectors. Amount of consumption of each alternative. One element per alternative, as long as the number of observations or a scalar. Names must match those in |
budget |
Numeric vector. Budget for each observation. |
functionality |
Character. Can take different values depending on desired output.
|
minConsumption |
Named list of scalars or numeric vectors. Minimum consumption of the alternatives, if consumed. As many elements as alternatives. Names must match those in |
rows |
Boolean vector. Consideration of rows in the likelihood calculation, FALSE to exclude. Length equal to the number of observations (nObs). Default is |
The returned object depends on the value of argument functionality
as follows.
"estimate": vector/matrix/array. Returns the probabilities for the chosen alternative for each observation.
"prediction": A matrix with one row per observation, and means and s.d. of predicted consumptions.
"validate": Boolean. Returns TRUE if all tests are passed.
"zero_LL": Not applicable.
"conditionals": Same as "prediction".
"output": Same as "estimate" but also writes summary of choices into temporary file (later read by apollo_modelOutput
).
"raw": Same as "prediction".