List of settings for the DFT model. It should contain the following elements.

• alternatives: Named numeric vector. Names of alternatives and their corresponding value in choiceVar.

• avail: Named list of numeric vectors or scalars. Availabilities of alternatives, one element per alternative. Names of elements must match those in alternatives. Values can be 0 or 1.

• choiceVar: Numeric vector. Contains choices for all observations. It will usually be a column from the database. Values are defined in alternatives.

• attrValues: A named list with as many elements as alternatives. Each element is itself a named list of vectors of the alternative attributes for each observation (usually a column from the database). All alternatives must have the same attributes (can be set to zero if not relevant).

• altStart: A named list with as many elements as alternatives. Each elment can be a scalar or vector containing the starting preference value for the alternative.

• attrWeights: A named list with as many elements as attributes, or fewer. Each element is the weight of the attribute, and can be a scalar, a vector with as many elements as observations, or a matrix/array if random. They should add up to one for each observation and draw (if present), and will be re-scaled if they do not. attrWeights and attrScalings are incompatible, and they should not be both defined for an attribute. Default is 1 for all attributes.

• attrScalings: A named list with as many elements as attributes, or fewer. Each element is a factor that scale the attribute, and can be a scalar, a vector or a matrix/array. They do not need to add up to one for each observation. attrWeights and attrScalings are incompatible, and they should not be both defined for an attribute. Default is 1 for all attributes.

• procPars: A list containing the four DFT 'process parameters'

  • error_sd: Numeric scalar or vector. The standard deviation of the the error term in each timestep.

  • timesteps: Numeric scalar or vector. Number of timesteps to consider. Should be an integer bigger than 0.

  • phi1: Numeric scalar or vector. Sensitivity.

  • phi2: Numeric scalar or vector. Process parameter.

• rows: Boolean vector. Consideration of rows in the likelihood calculation, FALSE to exclude. Length equal to the number of observations (nObs). Default is "all", equivalent to rep(TRUE, nObs).

• componentName: Character. Name given to model component.

Character. Can take different values depending on desired output.

• "estimate": Used for model estimation.

• "prediction": Used for model predictions.

• "validate": Used for validating input.

• "zero_LL": Used for calculating null likelihood.

• "conditionals": Used for calculating conditionals.

• "output": Used for preparing output after model estimation.

• "raw": Used for debugging.