| Title: | Designing Stated Preference Experiments |
|---|---|
| Description: | Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163). |
| Authors: | Erlend Dancke Sandorf [aut, cre], Danny Campbell [aut] |
| Maintainer: | Erlend Dancke Sandorf <[email protected]> |
| License: | CC BY-SA 4.0 |
| Version: | 0.0.5 |
| Built: | 2024-10-19 03:29:31 UTC |
| Source: | CRAN |
The function is called when the package is loaded through library or require.
.onAttach(libname, pkgname).onAttach(libname, pkgname)
libname |
Library name |
pkgname |
Package name |
Nothing
Check whether all priors and attributes have specified levels
all_priors_and_levels_specified(x)all_priors_and_levels_specified(x)
x |
A list of utility expressions |
A boolean equal to 'TRUE' if all are specified and 'FALSE' if not
Check whether any priors or attributes are specified with a value more than once
any_duplicates(x)any_duplicates(x)
x |
A list of utility expressions |
A boolean equal to 'TRUE' if specified more than once.
Check whether we can achieve attribute level balance
attribute_level_balance(x, rows)attribute_level_balance(x, rows)
x |
A list of utility expressions |
rows |
The number of rows in the design |
A boolean equal to 'TRUE' if attribute level balance can be achieved and 'FALSE' otherwise
Generic for getting the attributes and levels from the utility function
attribute_levels(x)attribute_levels(x)
x |
An object of class utility |
A named list of attribute levels
Generic for getting the attribute names
attribute_names(x)attribute_names(x)
x |
An object of class utility |
A character vector of attribute names
The function will take an object of class 'spdesign' and add a blocking column to the design matrix. The function will use random permutations of the blocking column to find the column that minimizes correlation between the blocking column and the design columns. Specifically the target for the minimization procedure is the mean squared correlation.
block(x, blocks, target = 5e-04, max_iter = 1e+06)block(x, blocks, target = 5e-04, max_iter = 1e+06)
x |
An object of class 'spdesign' |
blocks |
An integer giving the number of blocks. The number of blocks must be a multiple of the number of rows to ensure equal number of choices within a block. |
target |
A target value for the mean squared correlation. The default value is 0.0005. Setting the target to 0 forces the function to search all 'max_iter' blocking candidates |
max_iter |
The maximum number of candidates to consider before returning the best blocking candidate. The default value is 1000000. |
The function uses a random permutation so every time you run the function you will get a slightly different blocking column. You can set a seed prior to calling the function to always return the same blocking vector.
If you pass in a design that already contains a blocking column, then this blocking column will be replaced without warning.
A modified 'spdesign' object where the design is replaced with the same design and a blocking column. In addition a correlation vector, number of iterations and the target value are returned as part of the modified 'spdesign' object.
Computes the A-error of the design, which is equal to the trace of the variance-covariance matrix over the number of parameters to be estimated
calculate_a_error(design_vcov)calculate_a_error(design_vcov)
design_vcov |
A variance-covariance matrix returned by
|
A single error measure
Seeks to minimize the variance of the ratio of two parameters, for example, willingness-to-pay.
calculate_c_error(design_vcov, p, dudx, return_all)calculate_c_error(design_vcov, p, dudx, return_all)
design_vcov |
A variance-covariance matrix returned by
|
p |
Prior values |
dudx |
A character string giving the name of the prior in the denominator. Must be specified when optimizing for 'c-error' |
return_all |
If 'TRUE' return a K or K-1 vector with parameter specific error measures. Default is 'FALSE'. |
A vector giving the variance of the ratio for each K-1 parameter or a single number with the sum of the variances used for optimization
Computes the D-error of the design, which is equal to the K-root of the determinant of the variance-covariance matrix.
calculate_d_error(design_vcov)calculate_d_error(design_vcov)
design_vcov |
A variance-covariance matrix returned by
|
A single number
The function is called inside evaluate_design_candidate
calculate_efficiency( prior_values, design_env, model, dudx, return_all = FALSE, significance = 1.96 )calculate_efficiency( prior_values, design_env, model, dudx, return_all = FALSE, significance = 1.96 )
prior_values |
a list or vector of assumed priors |
design_env |
A design environment in which to evaluate the the function to derive the variance-covariance matrix. |
model |
A character string indicating the model to optimize the design for. Currently the only model programmed is the 'mnl' model and this is also set as the default. |
dudx |
A character string giving the name of the prior in the denominator. Must be specified when optimizing for 'c-error' |
return_all |
If 'TRUE' return a K or K-1 vector with parameter specific error measures. Default is 'FALSE'. |
significance |
A t-value corresponding to the desired level of significance. The default is significance at the 5 t-value of 1.96. |
A list with a named vector of efficiency criteria and the variance-covariance matrix
The function is a wrapper around calculate_a_error,
calculate_c_error, calculate_d_error and
calculate_s_error to provide a unified interface for
calling and calculating efficiency criteria.
calculate_efficiency_criteria( design_vcov, p, dudx, return_all = FALSE, significance = 1.96, type )calculate_efficiency_criteria( design_vcov, p, dudx, return_all = FALSE, significance = 1.96, type )
design_vcov |
A variance-covariance matrix returned by
|
p |
Prior values |
dudx |
A character string giving the name of the prior in the denominator. Must be specified when optimizing for 'c-error' |
return_all |
If 'TRUE' return a K or K-1 vector with parameter specific error measures. Default is 'FALSE'. |
significance |
A t-value corresponding to the desired level of significance. The default is significance at the 5 t-value of 1.96. |
type |
A string indicating the type of efficiency criteria to calculate can be either: "a-error", "c-error", "d-error" or "s-error" |
The function is mainly used internally to evaluate and report on designs, but is exported to allow the user to use the function to calculate the efficiency criteria of the model once it has been run on their data.
See individual efficiency criteria
Bliemer and Rose, 2009, Efficiency and sample size requirements for state choice experiments, Transportation Research Board Annual Meeting, Washington DC Scarpa and Rose, 2008, Designs efficiency for non-market valuation with choice modelling: How to measure it, what to report and why, Australian Journal of Agricultural and Resource Economics, 52(3):253-282 Bliemer and Rose, 2005a, Efficiency and sample size requirements for stated choice experiments, Report ITLS-WP-05-08, Institute for Transport and Logistics Studies, University of Sydney Kessels, R., Goos, P. and Vandebroek, M., 2006, A comparison of criteria to design efficient choice experiments, Journal of Marketing Research, 43(3):409-419
Calculates a "lower bound" sample size to obtain theoretically significant parameter estimates under the assumption that the priors are correct.
calculate_s_error(design_vcov, p, return_all, significance)calculate_s_error(design_vcov, p, return_all, significance)
design_vcov |
A variance-covariance matrix returned by
|
p |
Prior values |
return_all |
If 'TRUE' return a K or K-1 vector with parameter specific error measures. Default is 'FALSE'. |
significance |
A t-value corresponding to the desired level of significance. The default is significance at the 5 t-value of 1.96. |
A vector giving the "minimum" sample size for each parameter or a single number with the smallest sample size needed for all parameters to be theoretically significant.
The function cleans the utility expression by removing extra white spaces, removes brackets and other information to return a clean, easy-to-read expression.
clean_utility(x)clean_utility(x)
x |
An object of class utility |
We can also use the side-effect of the function on a list of utility expressions that do not contain brackets to return a an updated utility expression with alternative specific attribute names.
Warning: The function does not check if the utility expression *is* clean,
which means that running the function multiple times will result in
duplicate alternative names for the attributes. You need to pay particular
attention to this fact when using the formula update_utility
because this function calls clean_utility.
A cleaned utility function as a list
Generic for extracting the vector of priors
## S3 method for class 'spdesign' coef(object, ...)## S3 method for class 'spdesign' coef(object, ...)
object |
A model object of class 'spdesign' |
... |
Additional arguments passed to the function |
A vector of named priors used in the optimization
We are splitting on all separators first before detecting whether we have dummy coded attributes to allow for people reusing the _dummy name for the attribute.
contains_dummies(string)contains_dummies(string)
string |
A string or list of strings |
A boolean equal to 'TRUE' if the utility function contains dummy coded attributes and 'FALSE' otherwise
Calculate the correlation of the design. The function gets the design from
the design object before passing it to cor from stats.
This is a wrapper around cor.
cor(x, ...)cor(x, ...)
x |
A model object of class 'spdesign' |
... |
Additional parameters passed to the function |
Note that when your design includes constants, the function will print a warning because the standard deviation of a constant is 0.
A matrix with correlations
Cycles the attribute levels to create a new design candidate. "Cycling replaces all attribute levels in each choice situation at the time by replacing the first level with the second level, second level with the third etc. Since this change affects all columns, cycling can only be performed if all attributes have exactly the same sets of feasible levels, (e.g., where all variables are dummy coded)." (p. 253).
cycle(x)cycle(x)
x |
A vector of attribute levels |
This part of the RSC algorithm is rarely invoked.
A cycled design candidate
Hensher, D. A., Rose, J. M. & Greene, W., 2005, Applied Choice Analysis, 2nd ed., Cambridge University Press
Defines the base of the x_j list using the parsed utility expression, design_candidate and the base model matrix
define_base_x_j(utility, design_candidate)define_base_x_j(utility, design_candidate)
utility |
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs. |
design_candidate |
The current design candidate under consideration |
A base list x_j with model matrices the lenght of J
Define x_j to use for the analytic derivatives of the variance-covariance matrix. x_j is derived based on the provided utility functions and design candidate using base model.matrix to automatically handle alternative specific attributes and interaction terms
define_x_j(utility, design_candidate)define_x_j(utility, design_candidate)
utility |
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs. |
design_candidate |
The current design candidate under consideration |
We can extract the attribute names for each utility function to allow us
to place the correct restrictions on the design candidate. Specifically, we
restrict all levels of unavailable attributes to zero for alternatives where
they do not feature. This is to ensure that we do not give weight when
deriving the variance-covariance matrix using derive_vcov.
Furthermore, the Xs are "sorted" using the order of the candidate set, which
ensures that when we calculate the sum of the probabilities times X, the
correct columns are added together. See derive_vcov.
The list x_j
The function is a wrapper around derive_vcov_mnl and
derive_vcov_rpl and calculates the variance-covariance matrix
of the specified model and design given the priors.
derive_vcov(design_env, model)derive_vcov(design_env, model)
design_env |
An environment containing all the elements necessary to derive the variance-covariance matrix |
model |
A string indicating the model for which you wish to derive the variance covariance matrix. Can be either "mnl" or "rpl" |
The variance covariance matrix. If the Fisher information matrix is singular, then return NULL
The function takes no arguments and is evaluated in context!
derive_vcov_mnl()derive_vcov_mnl()
The variance co-variance matrix
The function takes no arguments and is evaluated in context!
derive_vcov_rpl()derive_vcov_rpl()
The variance co-variance matrix
Equation 1 in Bhat (2003)
digitize(n_dim, primes, count, digit)digitize(n_dim, primes, count, digit)
n_dim |
Number of dimensions |
primes |
A vector of prime numbers |
count |
A matrix |
digit |
A vector |
Bhat, C. n_draws., 2003, Simulation Estimation of Mixed Discrete Choice Models Using Randomized and Scrambled Halton Sequences, Transportation Research Part B, 9, pp. 837-855
The function will find the position of the dummy coded attributes in the candidate set (in the case of the Modified Federov or Random algorithms) or the design candidate (in the case of the RSC algorithm). This will let us know which columns to coerce to factors prior to defining x_j.
dummy_names(x)dummy_names(x)
x |
An object of class utility |
A boolean vector matching the expanded utility expression
The evaluation of the design candidate is independent of the optimization algorithm used.
evaluate_design_candidate( utility, design_candidate, prior_values, design_env, model, dudx, return_all, significance )evaluate_design_candidate( utility, design_candidate, prior_values, design_env, model, dudx, return_all, significance )
utility |
A utility function |
design_candidate |
The current design candidate |
prior_values |
a list or vector of assumed priors |
design_env |
A design environment in which to evaluate the the function to derive the variance-covariance matrix. |
model |
A character string indicating the model to optimize the design for. Currently the only model programmed is the 'mnl' model and this is also set as the default. |
dudx |
A character string giving the name of the prior in the denominator. Must be specified when optimizing for 'c-error' |
return_all |
If 'TRUE' return a K or K-1 vector with parameter specific error measures. Default is 'FALSE'. |
significance |
A t-value corresponding to the desired level of significance. The default is significance at the 5 t-value of 1.96. |
A named vector with efficiency criteria of the current design candidate. If Bayesian prior_values are used, then it returns the average error.
The function takes the list of exclusions and transforms them into an expression that is then parsed and evaluated to apply the exclusions to the supplied candidate set using standard subsetting routines.
exclude(candidate_set, exclusions)exclude(candidate_set, exclusions)
candidate_set |
A matrix or data frame in the "wide" format containing all permitted combinations of attributes. The default is NULL. If no candidate set is provided, then the full factorial subject to specified exclusions will be used. This is passed in as an object and not a character string. The candidate set will be expanded to include zero columns to consider alternative specific attributes. |
exclusions |
A list of exclusions Often this list will be pulled directly from the list of options or it is a modified list of exclusions |
A restricted candidate set
Expands the attributes and levels to the wide format. The nested list is padded with zeros where alternative specific attributes are present to ensure that we can work with square matrices.
expand_attribute_levels(x)expand_attribute_levels(x)
x |
An object of class utility |
A named vector
Extracts all parameter and attribute names from the utility function.
This is a wrapper around str_extract_all with a specified
boundary. The function also calls remove_all_brackets to
ensure that if a word is used inside a square bracket, e.g. seq, it is not
extracted.
extract_all_names(string, simplify = FALSE)extract_all_names(string, simplify = FALSE)
string |
A character string |
simplify |
If TRUE return as a vector. Default is FALSE. |
Note that we are not matching spaces nor the interaction operator I(). This is to avoid I being identified as its own (unspecified) attribute.
A list or vector with all names
Extracts attribute names. It is a wrapper around
extract_all_names and extract_param_names.
extract_attribute_names(string, simplify = FALSE)extract_attribute_names(string, simplify = FALSE)
string |
A character string |
simplify |
If TRUE return as a vector. Default is FALSE. |
A Vector or string wtih attribute names
This function will locate and extract the the distributions for Bayesian priors and random parameters as specified in the design. The output is used to create the matrix of correct draws for priors and parameters.
extract_distribution(string, type)extract_distribution(string, type)
string |
A single character string or list of character strings with a single or multiple utility functions |
type |
A string indicating the type: prior or param |
IMPORTANT: The function will silently drop duplicates.
A named vector of priors or parameters where the type of distribution is given by a character letter: "normal", "lognormal", "uniform" or "triangular"
The function extracts how many times each level of an attribute should occur within the design when attribute level balance is not enforced. Note that it extracts the parentheses AFTER the end of the square brackets. Specifying round brackets without the square brackets are syntactically invalid and therefore we want the code to fail in this case.
extract_level_occurrence(string, simplify = FALSE)extract_level_occurrence(string, simplify = FALSE)
string |
A character string |
simplify |
If TRUE return as a vector. Default is FALSE. |
The function extracts the named values of the supplied utility function.
extract_named_values(string)extract_named_values(string)
string |
A character string |
A named list of parameter and attribute values. Each list element is named and can contain a single prior, a list with a mean and sd, or a vector with attribute levels
Extract the parameter distribution
extract_param_distribution(string)extract_param_distribution(string)
string |
A single character string or list of character strings with a single or multiple utility functions |
Extracts all words starting with "b_". Leverages the fact that all parameters has to start with "b_".
extract_param_names(string, simplify = FALSE)extract_param_names(string, simplify = FALSE)
string |
A character string |
simplify |
If TRUE return as a vector. Default is FALSE. |
A list or vector with the parameter names.
Extract the prior distribution
extract_prior_distribution(string)extract_prior_distribution(string)
string |
A single character string or list of character strings with a single or multiple utility functions |
Only extract parameters and attributes with specified priors and levels. This is very useful to test whether parameters or attributes are specified multiple times
extract_specified(string, simplify = FALSE)extract_specified(string, simplify = FALSE)
string |
A character string |
simplify |
If TRUE return as a vector. Default is FALSE. |
If the utility function contains parameters that are dummy coded, the dummy coding is handled here. By expanding the dummy coding prior to parsing we can directly consider Bayesian priors for each level.
extract_unparsed_values(string)extract_unparsed_values(string)
string |
A character string |
A named list of parameter and attribute values. Each list element is named and contains a numeric value or expression to be parsed
Extracts the value argument(s) of the supplied string. The value argument is defined as the characters between [] string.
extract_values(string, simplify = FALSE)extract_values(string, simplify = FALSE)
string |
A character string |
simplify |
If TRUE return as a vector. Default is FALSE. |
A vector or list with the extracted value arguments
The modified Federov algorithm implemented here starts with a random design candidate and systematically swaps out rows of the design candidate to iteratively find better designs. The algorithm has the following steps and restrictions.
federov( design_object, model, efficiency_criteria, utility, prior_values, dudx, candidate_set, rows, control )federov( design_object, model, efficiency_criteria, utility, prior_values, dudx, candidate_set, rows, control )
design_object |
A list of class 'spdesign' created within the
|
model |
A character string indicating the model to optimize the design for. Currently the only model programmed is the 'mnl' model and this is also set as the default. |
efficiency_criteria |
A character string giving the efficiency criteria to optimize for. One of 'a-error', 'c-error', 'd-error' or 's-error'. No default is set and argument must be specified. Optimizing for multiple criteria is not yet implemented and will result in an error. |
utility |
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs. |
prior_values |
A list of priors |
dudx |
A character string giving the name of the prior in the denominator. Must be specified when optimizing for 'c-error' |
candidate_set |
A matrix or data frame in the "wide" format containing all permitted combinations of attributes. The default is NULL. If no candidate set is provided, then the full factorial subject to specified exclusions will be used. This is passed in as an object and not a character string. The candidate set will be expanded to include zero columns to consider alternative specific attributes. |
rows |
An integer giving the number of rows in the final design |
control |
A list of control options |
1) Create a random initial design and evaluate it. 2) Swap the first row of the design candidate with the first row of the candidate set. 3) If no better candidate is found, try the second row of the candidate set. Keep trying new rows of the candidate set until an improvement is found. NOTE: The candidate row will be checked against all rows of the design candidate to ensure that the same row is not included multiple times. 4) If a better candidate is found, then we try to swap out the next row in the design candidate with the first row of the candidate set. Keep repeating the previous step. 5) When all rows of the design candidate has been swapped once, reset the counter and work through the design candidate and candidate set again. 6) The algorithm terminates after a pre-determined number of iterations or when a pre-determined efficiency threshold has been found.
NOTE: I have not yet implemented a duplicate check! That is, I do not check whether the "same" choice rows are included but with the order of alternatives swapped. This can be achieved by further restricting the candidate set prior to searching for designs. That said, "identical" choice rows will not provide much additional information and should be excluded by default in the search process.
A list of class 'spdesign'
Test whether a design candidate fits the constraints imposed by the level occurrences
fits_lvl_occurrences(utility, x, rows)fits_lvl_occurrences(utility, x, rows)
utility |
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs. |
x |
An object of class 'utility' or 'spdesign' |
rows |
Number of rows in the design |
A boolean equal to TRUE if attribute level balanced
The function is a wrapper around expand.grid and
generates the full factorial given the supplied attributes. The attributes
can either be specified directly by the user or extracted from the list
of utility functions using.
full_factorial(attrs)full_factorial(attrs)
attrs |
A named list of attributes and their levels |
The full factorial is often used as the starting point to generate a
candidate set. Note that the full factorial will include unrealistic and
completely dominated alternatives. It is therefore advised to use a subset
of the full factorial as a candidate set. The user can call
full_factorial and create a subset that is passed to
generate_design using the 'candidate_set' parameter, or supply
a set of restrictions through the 'restrictions' argument.
A matrix containing the full factorial
opts <- list( level_balance = FALSE, tasks = 10 ) attrs <- list( a1 = 1:5, a2 = c(0, 1) ) full_factorial(attrs) V <- list( alt1 = "b_a1[0.1] * a1[1:5] + b_a2[-2] * a2[c(0, 1)]", alt2 = "b_a1 * a1 + b_a2 * a2" ) attrs <- expand_attribute_levels(V) full_factorial(attrs)opts <- list( level_balance = FALSE, tasks = 10 ) attrs <- list( a1 = 1:5, a2 = c(0, 1) ) full_factorial(attrs) V <- list( alt1 = "b_a1[0.1] * a1[1:5] + b_a2[-2] * a2[c(0, 1)]", alt2 = "b_a1 * a1 + b_a2 * a2" ) attrs <- expand_attribute_levels(V) full_factorial(attrs)
The function generates efficient experimental designs. The function takes a set of indirect utility functions and generates efficient experimental designs assuming that people are maximizing utility.
generate_design( utility, rows, model = "mnl", efficiency_criteria = c("a-error", "c-error", "d-error", "s-error"), algorithm = c("federov", "rsc", "random"), draws = c("pseudo-random", "mlhs", "standard-halton", "scrambled-halton", "standard-sobol", "scrambled-sobol"), R = 100, dudx = NULL, candidate_set = NULL, exclusions = NULL, control = list(cores = 1, max_iter = 10000, max_relabel = 10000, max_no_improve = 1e+05, efficiency_threshold = 0.1, sample_with_replacement = FALSE) )generate_design( utility, rows, model = "mnl", efficiency_criteria = c("a-error", "c-error", "d-error", "s-error"), algorithm = c("federov", "rsc", "random"), draws = c("pseudo-random", "mlhs", "standard-halton", "scrambled-halton", "standard-sobol", "scrambled-sobol"), R = 100, dudx = NULL, candidate_set = NULL, exclusions = NULL, control = list(cores = 1, max_iter = 10000, max_relabel = 10000, max_no_improve = 1e+05, efficiency_threshold = 0.1, sample_with_replacement = FALSE) )
utility |
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs. |
rows |
An integer giving the number of rows in the final design |
model |
A character string indicating the model to optimize the design for. Currently the only model programmed is the 'mnl' model and this is also set as the default. |
efficiency_criteria |
A character string giving the efficiency criteria to optimize for. One of 'a-error', 'c-error', 'd-error' or 's-error'. No default is set and argument must be specified. Optimizing for multiple criteria is not yet implemented and will result in an error. |
algorithm |
A character string giving the optimization algorithm to use. No default is set and the argument must be specified to be one of 'rsc', 'federov' or 'random'. |
draws |
The type of draws to use with Bayesian priors. No default is set and must be specified even if you are not creating a Bayesian design. Can be one of "pseudo-random", "mlhs", "standard-halton", "scrambled-halton", "standard-sobol","scrambled-sobol". |
R |
An integer giving the number of draws to use. The default is 100. |
dudx |
A character string giving the name of the prior in the denominator. Must be specified when optimizing for 'c-error' |
candidate_set |
A matrix or data frame in the "wide" format containing all permitted combinations of attributes. The default is NULL. If no candidate set is provided, then the full factorial subject to specified exclusions will be used. This is passed in as an object and not a character string. The candidate set will be expanded to include zero columns to consider alternative specific attributes. |
exclusions |
A list of exclusions Often this list will be pulled directly from the list of options or it is a modified list of exclusions |
control |
A list of control options |
No assumptions are made with respect to default values and it is up to the user to specify optimization criteria, optmization routines, draws to use for Bayesian priors and more.
An object of class 'spdesign'
Creates a design candidate by assuming attribute level balance. Will work out the minimum level of times an attribute must occur for level balance. If level balance cannot be achieved the function will systematically add level occurrences to get as close as possible to attribute level balance.
generate_rsc_candidate(utility, rows)generate_rsc_candidate(utility, rows)
utility |
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs. |
rows |
An integer giving the number of rows in the final design |
A data.frame with rows equal to the number of choice tasks and columns equal to the number of attributes in the 'wide' format
This is particularly useful for flow-control
has_bayesian_prior(string)has_bayesian_prior(string)
string |
A string or list of strings |
A boolean equal to 'TRUE' if we have Bayesian priors
This is particularly useful for flow-control
has_random_parameter(string)has_random_parameter(string)
string |
A string or list of strings |
A boolean equal to 'TRUE' if we have random parameters
Tests whether there is an equal number of opening and closing brackets in the utility functions.
is_balanced(string, open, close)is_balanced(string, open, close)
string |
A character string |
open |
An opening bracket ( [ or < |
close |
A closing bracket ) ] or > |
A boolean equal to 'TRUE' if the utility expression is balanced
Prints a table of level balance for your design. If the design is blocked you will get both level balance per block and overall level balance
level_balance(design, block = FALSE)level_balance(design, block = FALSE)
design |
An spdesign object |
block |
A boolean equal to TRUE if you want frequency tables per block. The default value is FALSE |
Creates a list of lookup tables for attribute level occurrence.
lvl_occurrences(utility, rows, level_balance)lvl_occurrences(utility, rows, level_balance)
utility |
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs. |
rows |
An integer giving the number of rows in the final design |
level_balance |
Boolean equal to TRUE if level balance. This is not used |
A list the length of the expanced attribute levels. Each list element is a lookup table where the names of the table is the attribute level and the element the number of times the minimum number of times the level occurs.
A common interface to creating a variety of random draws used to simulate the log likelihood function
make_draws(n_ind, n_draws, n_dim, seed, type)make_draws(n_ind, n_draws, n_dim, seed, type)
n_ind |
Number of individuals in your sample |
n_draws |
Number of draws per respondent |
n_dim |
Number of dimensions |
seed |
A seed to change the scrambling of the sobol sequence. |
type |
A character string |
A matrix of dimensions n_ind*n_draws x n_dim of standard uniform draws
n_ind <- 10 n_draws <- 5 n_dim <- 3 draws <- make_draws(n_ind, n_draws, n_dim, seed = 10, "scrambled-sobol") head(draws) draws <- make_draws(n_ind, n_draws, n_dim, seed = 10, "scrambled-halton") head(draws)n_ind <- 10 n_draws <- 5 n_dim <- 3 draws <- make_draws(n_ind, n_draws, n_dim, seed = 10, "scrambled-sobol") head(draws) draws <- make_draws(n_ind, n_draws, n_dim, seed = 10, "scrambled-halton") head(draws)
Make Modified Latin Hypercube Draws
make_mlhs(n_ind, n_draws, n_dim)make_mlhs(n_ind, n_draws, n_dim)
n_ind |
Number of individuals in your sample |
n_draws |
Number of draws per respondent |
n_dim |
Number of dimensions |
Hess, S., Train, K. E. & Polak, J. W., 2006, On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice, Transportation Research Part B, 40, pp. 147-163
Wrapper for runif to create a common interface
make_pseudo_random(n_ind, n_draws, n_dim)make_pseudo_random(n_ind, n_draws, n_dim)
n_ind |
Number of individuals in your sample |
n_draws |
Number of draws per respondent |
n_dim |
Number of dimensions |
A function for creating scrambled Halton draws. The code is a translation of the [GAUSS](http://www.caee.utexas.edu/prof/bhat/FULL_CODES.htm) codes written by Professor Chandra Bhat. Note that the maximum number of dimensions for the scrambled Halton draws is limited to 16. This is because only permutations up to prime 16 are included in the permutation matrix. Extending to more than 16 dimensions can be achieved by including a different permutation matrix.
make_scrambled_halton(n_ind, n_draws, n_dim)make_scrambled_halton(n_ind, n_draws, n_dim)
n_ind |
Number of individuals in your sample |
n_draws |
Number of draws per respondent |
n_dim |
Number of dimensions |
The permutations are based on the Braaten-Weller algorithm.
Bhat, C. n_draws., 2003, Simulation Estimation of Mixed Descrete Choice Models Using Randomized and Scrambled Halton Sequences, Transportation Research Part B, 9, pp. 837-855
Wrapper function for sobol() from randtoolbox to create a common interface. Owen + Fazure_Tezuka Scrambling
make_scrambled_sobol(n_ind, n_draws, n_dim, seed = seed)make_scrambled_sobol(n_ind, n_draws, n_dim, seed = seed)
n_ind |
Number of individuals in your sample |
n_draws |
Number of draws per respondent |
n_dim |
Number of dimensions |
seed |
A seed to change the scrambling of the sobol sequence. |
Wrapper function for halton() from randtoolbox to create a common interface
make_standard_halton(n_ind, n_draws, n_dim)make_standard_halton(n_ind, n_draws, n_dim)
n_ind |
Number of individuals in your sample |
n_draws |
Number of draws per respondent |
n_dim |
Number of dimensions |
Wrapper function for sobol() from randtoolbox to create a common interface
make_standard_sobol(n_ind, n_draws, n_dim, seed = seed)make_standard_sobol(n_ind, n_draws, n_dim, seed = seed)
n_ind |
Number of individuals in your sample |
n_draws |
Number of draws per respondent |
n_dim |
Number of dimensions |
seed |
A seed to change the scrambling of the sobol sequence. |
Find minimium level occurrences. This is useful to ensure/approximate attribute level balance in designs using the Modified Federov Algorithm or the Random design algorithms.
min_lvl_occurrence(x, rows)min_lvl_occurrence(x, rows)
x |
An object of class 'utility' or 'spdesign' |
rows |
Number of rows in the design |
A list of minimum level occurrences for the attribute levels
Find the number of levels for each attribute
nlvls(x)nlvls(x)
x |
An object of class 'utility' or 'spdesign' |
A list with the number of levels for each attribute
The function returns its arguments as a named list. The function is used
inside the utility functions. It is transformed to an expression using
parse and evaluated using eval. This ensures
that in the case of an RPL with Bayesian priors, recursion is handled
automatically. This significantly simplifies translating the utility function
to lists of parameters to use when optimizing the designs. It is also less
error prone.
normal(mu, sigma) normal_p(mu, sigma) lognormal(mu, sigma) lognormal_p(mu, sigma) triangular(mu, sigma) triangular_p(mu, sigma) uniform(mu, sigma) uniform_p(mu, sigma)normal(mu, sigma) normal_p(mu, sigma) lognormal(mu, sigma) lognormal_p(mu, sigma) triangular(mu, sigma) triangular_p(mu, sigma) uniform(mu, sigma) uniform_p(mu, sigma)
mu |
A parameter indicating the mean or location of the distribution
depending on whether it is a normal, log-normal, triangular or uniform,
or it can be another call to |
sigma |
A parameter indicating the SD or spread of the distribution
or it can be another call to |
A list of parameters
normal(): The normal distribution
normal_p(): The normal distribution when applied to a prior
lognormal(): The log normal distribution
lognormal_p(): The log-normal distribution when applied to a prior
triangular(): The triangular distribution
triangular_p(): The triangular distribution when applied to a prior
uniform(): The uniform distribution
uniform_p(): The uniform distribution when applied to a prior
This function will set the range of attribute level occurrences equal to to the size of the design. This is equivalent to fully letting go of attribute level balance. Letting go of attribute level balance is the default behavior for the Modified Federov algorithm and the Random algorithm.
occurrences(x, rows)occurrences(x, rows)
x |
An object of class 'utility' or 'spdesign' |
rows |
Number of rows in the design |
If restrictions are placed on attribute level occurrence in the utility function, then this function will extract these and add them to the output.
Notice that specifying restrictions in the utility function only matters for the Modified Federov and Random algorithms and will in general result in a less efficient design.
A named list of lists where the outer list is for the attributes and the inner list, the levels of each attribute and the number or range of times they can occur
Prepare the list of priors
prepare_priors(utility, draws, R)prepare_priors(utility, draws, R)
utility |
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs. |
draws |
The type of draws to use with Bayesian priors. No default is set and must be specified even if you are not creating a Bayesian design. Can be one of "pseudo-random", "mlhs", "standard-halton", "scrambled-halton", "standard-sobol","scrambled-sobol". |
R |
An integer giving the number of draws to use. The default is 100. |
A list of priors
The function prints a string of efficiency criteria to the console and
highlights the color of the considered efficiency criteria. Effectively it
is a wrapper around multiple calls to cat.
print_efficiency_criteria( iter, values, criteria, digits = 4, padding = 10, efficiency_criteria )print_efficiency_criteria( iter, values, criteria, digits = 4, padding = 10, efficiency_criteria )
iter |
An integer giving the iteration of the loop |
values |
The value of the efficiency criteria obtained by
|
criteria |
A character string with the name of the efficiency criteria. See manual for valid values |
digits |
The nubmer of digits to round the printed value to. The default is 4. |
padding |
An integer specifying the padding of each column element. Default value is 10. |
efficiency_criteria |
The criteria that we optimize over |
A character string.
The function prints the initial header for the console output and colors in
the criteria used for optimization. Effectively, the function makes multiple
calls to cat.
print_initial_header(efficiency_criteria, padding = 10, width = 80)print_initial_header(efficiency_criteria, padding = 10, width = 80)
efficiency_criteria |
The criteria that we optimize over |
padding |
An integer specifying the padding of each column element. Default value is 10. |
width |
An integer giving the width of the horizontal rules. Default value is 80 |
Noting
Prints iteration information every time a better design is found. The
function wraps around print_initial_header and
print_efficiency_criteria. This reduces the number of
if-statements and function calls within generate_design in an
attempt simplify code maintenance.
print_iteration_information( iter, values, criteria, digits = 4, padding = 10, width = 80, efficiency_criteria )print_iteration_information( iter, values, criteria, digits = 4, padding = 10, width = 80, efficiency_criteria )
iter |
An integer giving the iteration of the loop |
values |
The value of the efficiency criteria obtained by
|
criteria |
A character string with the name of the efficiency criteria. See manual for valid values |
digits |
The nubmer of digits to round the printed value to. The default is 4. |
padding |
An integer specifying the padding of each column element. Default value is 10. |
width |
An integer giving the width of the horizontal rules. Default value is 80 |
efficiency_criteria |
The criteria that we optimize over |
Nothing
A generic function for printing an 'spdesign' object
## S3 method for class 'spdesign' print(x, ...)## S3 method for class 'spdesign' print(x, ...)
x |
A model object of class 'spdesign' |
... |
Additional parameters passed to the function |
No return value. Prints the 'spdesign' object.
Generic for extracting the vector of priors
priors(x)priors(x)
x |
An object of class 'utility' or 'spdesign' |
A list of named priors used in the optimization
Will take the design object and calculate the probabilities of each alternative and choice tasks.
probabilities(x)probabilities(x)
x |
An 'spdesign' object. |
Using Bayesian priors the average across the prior distribution will be used.
Using the specific type of model, either the MNL or RPL probs will be returned.
A matrix of probabilities for each alternative and choice task.
Calculate the MNL probabilities
probabilities_mnl(x)probabilities_mnl(x)
x |
An 'spdesign' object. |
A matrix of probabilities for each alternative and choice task. With Bayesian priors the return is the average probabilites over the prior distribution
Equation 2 in Bhat (2003)
radical_inverse(n_dim, primes, count, digit, perms)radical_inverse(n_dim, primes, count, digit, perms)
n_dim |
Number of dimensions |
primes |
A vector of prime numbers |
count |
A matrix |
digit |
A vector |
perms |
A matrix of the permutations. Defaults to a set of Braaten-Weller permutations. |
Bhat, C. n_draws., 2003, Simulation Estimation of Mixed Descrete Choice Models Using Randomized and Scrambled Halton Sequences, Transportation Research Part B, 9, pp. 837-855
Generates a random design by sampling from the candidate set each update of the algorithm.
random( design_object, model, efficiency_criteria, utility, prior_values, dudx, candidate_set, rows, control )random( design_object, model, efficiency_criteria, utility, prior_values, dudx, candidate_set, rows, control )
design_object |
A list of class 'spdesign' created within the
|
model |
A character string indicating the model to optimize the design for. Currently the only model programmed is the 'mnl' model and this is also set as the default. |
efficiency_criteria |
A character string giving the efficiency criteria to optimize for. One of 'a-error', 'c-error', 'd-error' or 's-error'. No default is set and argument must be specified. Optimizing for multiple criteria is not yet implemented and will result in an error. |
utility |
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs. |
prior_values |
A list of priors |
dudx |
A character string giving the name of the prior in the denominator. Must be specified when optimizing for 'c-error' |
candidate_set |
A matrix or data frame in the "wide" format containing all permitted combinations of attributes. The default is NULL. If no candidate set is provided, then the full factorial subject to specified exclusions will be used. This is passed in as an object and not a character string. The candidate set will be expanded to include zero columns to consider alternative specific attributes. |
rows |
An integer giving the number of rows in the final design |
control |
A list of control options |
With no restrictions placed, this type of design will only consider efficiency. There is no guarantee that you will achieve attribute level balance, nor that all attribute levels will be present. More efficient designs tend to have more extreme trade-offs.
A list of class 'spdesign'
Sample from the candidate set to create a random design_object.
random_design_candidate(utility, candidate_set, rows, sample_with_replacement)random_design_candidate(utility, candidate_set, rows, sample_with_replacement)
utility |
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs. |
candidate_set |
A matrix or data frame in the "wide" format containing all permitted combinations of attributes. The default is NULL. If no candidate set is provided, then the full factorial subject to specified exclusions will be used. This is passed in as an object and not a character string. The candidate set will be expanded to include zero columns to consider alternative specific attributes. |
rows |
An integer giving the number of rows in the final design |
sample_with_replacement |
A boolean equal to TRUE if we sample from the candidate set with replacement. The default is FALSE |
Relabels the attribute levels to create a new design candidate. For example, if the column contains the levels (1, 2, 1, 3, 2, 3) and 1 and 3 are relabeled, then the column becomes (3, 2, 3, 1, 2, 1), i.e. 1 becomes 3 and 3 becomes 1.
relabel(x)relabel(x)
x |
A vector of attribute levels |
Will randomly sample 2 attribute levels that will be relabeled and the relabeling is done independently for each column, which implies that the same attribute will be relabeled differently depending on which alternative it belongs to.
Hensher, D. A., Rose, J. M. & Greene, W., 2005, Applied Choice Analysis, 2nd ed., Cambridge University Press
Takes a string as input and removes everything between square and round
brackets. The function wraps around remove_square_brackets and
remove_round_brackets. To avoid problems, we first remove
square brackets.
remove_all_brackets(string)remove_all_brackets(string)
string |
A character string |
A string without brackets
Removes the parameter from the utility string
remove_prior(prior, string)remove_prior(prior, string)
prior |
A string with the parameter name |
string |
A string to remove param from |
Removes everything between (and including) round brackets. We negating matches with I(), since this is R's interaction operator.
remove_round_brackets(string)remove_round_brackets(string)
string |
A character string |
(?<!I) - A negative lookbehind for I
Removes everything between (and including) square brackets
remove_square_brackets(string)remove_square_brackets(string)
string |
A character string |
Takes a string as an input and removes all whitespaces in the string
remove_whitespace(string)remove_whitespace(string)
string |
A character string |
A character vector with no white spaces
Repeats each column of the matrix or data frame 'x' a number of times equal to 'times'.
rep_cols(x, times)rep_cols(x, times)
x |
A matrix or data frame |
times |
An integer indicating the number of times to repeat the row/column |
A matrix or data.frame depending on the type of the input
test_matrix <- matrix(runif(12), 4) rep_cols(test_matrix, 2)test_matrix <- matrix(runif(12), 4) rep_cols(test_matrix, 2)
Repeats each row in the matrix or data frame 'x' a number of times equal to 'times'.
rep_rows(x, times)rep_rows(x, times)
x |
A matrix or data frame |
times |
An integer indicating the number of times to repeat the row/column |
A matrix or data.frame depending on type of the input
test_matrix <- matrix(runif(12), 4) rep_rows(test_matrix, 2)test_matrix <- matrix(runif(12), 4) rep_rows(test_matrix, 2)
Depending on the setting the function calls a combination of
relabel, swap and cycle
to create new design candidates. The code is intentionally written modular
to allow for all special cases of the algorithm.
rsc( design_object, model, efficiency_criteria, utility, prior_values, dudx, candidate_set, rows, control )rsc( design_object, model, efficiency_criteria, utility, prior_values, dudx, candidate_set, rows, control )
design_object |
A list of class 'spdesign' created within the
|
model |
A character string indicating the model to optimize the design for. Currently the only model programmed is the 'mnl' model and this is also set as the default. |
efficiency_criteria |
A character string giving the efficiency criteria to optimize for. One of 'a-error', 'c-error', 'd-error' or 's-error'. No default is set and argument must be specified. Optimizing for multiple criteria is not yet implemented and will result in an error. |
utility |
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs. |
prior_values |
A list of priors |
dudx |
A character string giving the name of the prior in the denominator. Must be specified when optimizing for 'c-error' |
candidate_set |
A matrix or data frame in the "wide" format containing all permitted combinations of attributes. The default is NULL. If no candidate set is provided, then the full factorial subject to specified exclusions will be used. This is passed in as an object and not a character string. The candidate set will be expanded to include zero columns to consider alternative specific attributes. |
rows |
An integer giving the number of rows in the final design |
control |
A list of control options |
The function sets the default level occurrence of an attribute when a design is restricted to be attribute level balanced. If the design cannot be attribute level balanced, then the restriction will be relaxed for each attribute failing to meet this criteria. Specifically, the code will impose a minimum range of how often an attribute level can occur. This will secure that the design is near attribute level balanced. In this case a warning is issued.
set_default_level_occurrence(n_lvls, rows)set_default_level_occurrence(n_lvls, rows)
n_lvls |
An integer giving the number of levels for the considered attribute |
rows |
Number of rows in the design |
A named list of lists where the top level gives the attribute and the lower level gives the times or range each attribute level should occur in the design
The function takes the list of design options and adds default values where
none are specified. This function is exported, but is not intended to be
called by the user of the package. The function is called from within
generate_design to populate the list with sensible defaults
set_default_options(opts_input)set_default_options(opts_input)
opts_input |
A list of user supplied design options |
A list of design options populated by sensible default values
Shuffle the order of points in the unit interval.
shuffle(x)shuffle(x)
x |
A vector |
Create a summary of the experimental design
## S3 method for class 'spdesign' summary(object, ...)## S3 method for class 'spdesign' summary(object, ...)
object |
A model object of class 'spdesign' |
... |
Additional arguments passed to the function |
No return value. Prints a summary of the 'spdesign' object to the console
Swaps the order of the attributes to create a new design candidate. For example, if the attributes in the first and fourth choice situation (row) are swapped, then (1, 2, 1, 3, 2, 3) becomes( 3, 2, 1, 1, 2, 3).
swap(x)swap(x)
x |
A vector of attribute levels |
The algorithm randomly samples 2 row positions that are swapped and the swaps are independent across attributes and alternatives
Hensher, D. A., Rose, J. M. & Greene, W., 2005, Applied Choice Analysis, 2nd ed., Cambridge University Press
Uses the formula of T * (J - 1) to check if the design is large enough to identify the parameters of the utility function.
too_small(x, rows)too_small(x, rows)
x |
A list of utility expressions |
rows |
The number of rows in the design |
A boolean equal to 'TRUE' if the design is too small
Transform distribution
transform_distribution(mu, sigma, eta, type)transform_distribution(mu, sigma, eta, type)
mu |
A value for the mean of the distribution |
sigma |
A value for the standard deviation of the distribution |
eta |
A numeric standard uniform vector |
type |
The type of distribution |
A vector with the transformed distribution given the parameters
Transform to the lognormal distribution
transform_lognormal(mu, sigma, eta)transform_lognormal(mu, sigma, eta)
mu |
A value for the mean of the distribution |
sigma |
A value for the standard deviation of the distribution |
eta |
A numeric standard uniform vector |
Transform to the normal distribution
transform_normal(mu, sigma, eta)transform_normal(mu, sigma, eta)
mu |
A value for the mean of the distribution |
sigma |
A value for the standard deviation of the distribution |
eta |
A numeric standard uniform vector |
Transform to the triangular distribution
transform_triangular(mu, sigma, eta)transform_triangular(mu, sigma, eta)
mu |
A value for the mean of the distribution |
sigma |
A value for the standard deviation of the distribution |
eta |
A numeric standard uniform vector |
Transform to the uniform distribution
transform_uniform(mu, sigma, eta)transform_uniform(mu, sigma, eta)
mu |
A value for the mean of the distribution |
sigma |
A value for the standard deviation of the distribution |
eta |
A numeric standard uniform vector |
Updates the utility function to consider dummy coded attributes. It will expand the dummy-coding to K-1 dropping the lowest level. This is consistent with standard practice.
update_utility(x)update_utility(x)
x |
An object of class utility |
The function is called prior to evaluating designs if dummy-coded attributes are present in the utility function. This is because the utility function is evaluated in the context of the design environment and must be added there
Important to note about the naming of the expanded priors and attributes: The names for the attributes will be attached with the level of the factor, whereas the prior will be named corresponding to the level, e.g., 2, 3, 4. This is simply the result of the difference between how it's extracted from the utility functions and how model.matrix creates names.
An updated cleaned utility expression
Create formulas from the utility functions such that we can create correct model matrices.
utility_formula(x)utility_formula(x)
x |
An object of class utility |
Note that this function should be used on a cleaned utility expression and
**not** an updated utility expression. This is because we are converting
dummy coded attributes to factors prior to calling model.matrix.
This ensures that dummy coded attributes are correctly returned with the
model matrix.
A list of formula expressions for the utility functions
A generic method for extracting the variance covariance matrix from a design object
## S3 method for class 'spdesign' vcov(object, ...)## S3 method for class 'spdesign' vcov(object, ...)
object |
A model object of class 'spdesign' |
... |
Additional arguments passed to the function |
A matrix with row- and column names equal to the parameter names