LatinHypercubeSampler: R6 class to represent a Latin hypercube sampler.
In poems: Pattern-Oriented Ensemble Modeling System
LatinHypercubeSampler R Documentation
R6 class to represent a Latin hypercube sampler.
Description
R6 class that generates Latin hypercube samples (using
randomLHS) for parameters drawn from configured
distributions: uniform, Poisson,
normal, lognormal,
beta, truncated normal or
triangular.
It generates a data frame of sample values.
Super class
poems::GenericClass -> LatinHypercubeSampler
Public fields
attachedA list of dynamically attached attributes (name-value pairs).
Active bindings
parameter_namesA vector of sample parameter names.
parameter_distributionsA list of sample distribution values (nested list with appropriate parameters).
Methods
Public methods
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Inherited methods
Method new()
Initialization method sets parameter names when provided.
Usage
LatinHypercubeSampler$new(parameter_names = NULL, ...)
Arguments
parameter_namesOptional vector of sample parameter names.
...Additional parameters passed individually.
Method set_class_parameter()
Sets a parameter to sampled from a vector of classes.
Usage
LatinHypercubeSampler$set_class_parameter(parameter_name, classes)
Arguments
parameter_nameCharacter string name of sample parameter.
classesVector of class values.
Method set_uniform_parameter()
Sets a parameter to be sampled from a uniform distribution with lower and upper bounds, optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_uniform_parameter(
parameter_name,
lower = 0,
upper = 1,
decimals = NULL
)
Arguments
parameter_nameCharacter string name of sample parameter.
lowerLower bound of the uniform distribution (default = 0).
upperUpper bound of the uniform distribution (default = 1).
decimalsOptional number of decimals applied to generated samples.
Method set_normal_parameter()
Sets a parameter to be sampled from a normal distribution with mean and standard deviation, optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_normal_parameter(
parameter_name,
mean = 0,
sd = 1,
decimals = NULL
)
Arguments
parameter_nameCharacter string name of sample parameter.
meanMean parameter for the normal distribution (default = 0).
sdStandard deviation parameter for the normal distribution (default = 1).
decimalsOptional number of decimals applied to generated samples.
Method set_poisson_parameter()
Sets a parameter to be sampled from a Poisson distribution with lambda parameter. Produces integers.
Usage
LatinHypercubeSampler$set_poisson_parameter(parameter_name, lambda = 1)
Arguments
parameter_nameCharacter string name of sample parameter.
lambdaLambda parameter for the Poisson distribution. Must be positive (default = 1).
Method set_lognormal_parameter()
Sets a parameter to be sampled from a lognormal distribution with log mean and log standard deviation, optionally expressed as regular mean and SD (overriding log mean/sd), and optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_lognormal_parameter(
parameter_name,
meanlog = 0,
sdlog = 1,
mean = NULL,
sd = NULL,
decimals = NULL
)
Arguments
parameter_nameCharacter string name of sample parameter.
meanlogLog mean parameter for the lognormal distribution (default = 0).
sdlogLog standard deviation parameter for the lognormal distribution (default = 1).
meanOptional (overriding) regular mean parameter for the lognormal distribution (default = NULL).
sdOptional (overriding) standard deviation parameter for the lognormal distribution (default = NULL).
decimalsOptional number of decimals applied to generated samples.
Method set_beta_parameter()
Sets a parameter to be sampled from a beta distribution configured with alpha and beta parameters, or optionally with mean and standard deviation (overriding alpha and beta), and optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_beta_parameter(
parameter_name,
alpha = 1,
beta = 1,
mean = NULL,
sd = NULL,
decimals = NULL
)
Arguments
parameter_nameCharacter string name of sample parameter.
alphaShaping (towards 1) parameter (> 0) for the beta distribution (default = 1).
betaShaping (towards 0) parameter (> 0) for the beta distribution (default = 1).
meanOptional (overriding) mean parameter for the beta distribution (default = NULL).
sdOptional (overriding) standard deviation parameter for the beta distribution (default = NULL).
decimalsOptional number of decimals applied to generated samples.
Method set_truncnorm_parameter()
Sets a parameter to be sampled from a truncated normal distribution with mean, standard deviation, and lower and upper bounds, optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_truncnorm_parameter(
parameter_name,
mean = 0,
sd = 1,
lower = -Inf,
upper = Inf,
decimals = NULL
)
Arguments
parameter_nameCharacter string name of sample parameter.
meanMean parameter of the truncated normal distribution (default = 0).
sdStandard deviation of the truncated normal distribution (default = 1).
lowerLower bound of the truncated normal distribution (default = -Inf, meaning no lower bound).
upperUpper bound of the truncated normal distribution (default = Inf, meaning no upper bound).
decimalsOptional number of decimals that generated samples are rounded to.
Method set_triangular_parameter()
Sets a parameter to be sampled from a triangular distribution with lower and upper bounds and mode (peak), optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_triangular_parameter(
parameter_name,
lower = 0,
upper = 1,
mode = (lower + upper)/2,
decimals = NULL
)
Arguments
parameter_nameCharacter string name of sample parameter.
lowerLower bound of the triangular distribution (default = 0).
upperUpper bound of the triangular distribution (default = 1).
modeMode (or peak) of the triangular distribution (default = (lower + upper)/2).
decimalsOptional number of decimals applied to generated samples.
Method generate_samples()
Generates Latin hypercube sample data (via randomLHS) for the set parameters using corresponding distributions.
Usage
LatinHypercubeSampler$generate_samples(number = 10, random_seed = NULL)
Arguments
numberNumber of samples to generate (default = 10).
random_seedOptional seed for the random generation of samples.
Returns
A data frame of generated sample values.
Method clone()
The objects of this class are cloneable with this method.
Usage
LatinHypercubeSampler$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
lhs_gen <- LatinHypercubeSampler$new(parameter_names = c("size", "age", "km", "price"))
lhs_gen$set_class_parameter("size", c("small", "medium", "large"))
lhs_gen$set_uniform_parameter("age", lower = 18, upper = 70, decimals = 0)
lhs_gen$set_poisson_parameter("offspring", lambda = 2)
lhs_gen$set_normal_parameter("km", mean = 50000, sd = 20000, decimals = 0)
lhs_gen$set_truncnorm_parameter("kg", mean = 75, sd = 20, lower = 0, upper = Inf, decimals = 2)
lhs_gen$set_lognormal_parameter("price", mean = 30000, sd = 10000, decimals = 0)
lhs_gen$set_beta_parameter("tread", mean = 0.7, sd = 0.1, decimals = 2)
lhs_gen$set_triangular_parameter("rating",
lower = 0, upper = 10, mode = 5,
decimals = 1
)
lhs_gen$generate_samples(number = 10, random_seed = 123)
poems documentation built on April 4, 2025, 5:07 a.m.
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| LatinHypercubeSampler | R Documentation |
R6 class to represent a Latin hypercube sampler.
Description
R6 class that generates Latin hypercube samples (using
randomLHS) for parameters drawn from configured
distributions: uniform, Poisson,
normal, lognormal,
beta, truncated normal or
triangular.
It generates a data frame of sample values.
Super class
poems::GenericClass -> LatinHypercubeSampler
Public fields
attachedA list of dynamically attached attributes (name-value pairs).
Active bindings
parameter_namesA vector of sample parameter names.
parameter_distributionsA list of sample distribution values (nested list with appropriate parameters).
Methods
Public methods
Inherited methods
Method new()
Initialization method sets parameter names when provided.
Usage
LatinHypercubeSampler$new(parameter_names = NULL, ...)
Arguments
parameter_namesOptional vector of sample parameter names.
...Additional parameters passed individually.
Method set_class_parameter()
Sets a parameter to sampled from a vector of classes.
Usage
LatinHypercubeSampler$set_class_parameter(parameter_name, classes)
Arguments
parameter_nameCharacter string name of sample parameter.
classesVector of class values.
Method set_uniform_parameter()
Sets a parameter to be sampled from a uniform distribution with lower and upper bounds, optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_uniform_parameter( parameter_name, lower = 0, upper = 1, decimals = NULL )
Arguments
parameter_nameCharacter string name of sample parameter.
lowerLower bound of the uniform distribution (default = 0).
upperUpper bound of the uniform distribution (default = 1).
decimalsOptional number of decimals applied to generated samples.
Method set_normal_parameter()
Sets a parameter to be sampled from a normal distribution with mean and standard deviation, optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_normal_parameter( parameter_name, mean = 0, sd = 1, decimals = NULL )
Arguments
parameter_nameCharacter string name of sample parameter.
meanMean parameter for the normal distribution (default = 0).
sdStandard deviation parameter for the normal distribution (default = 1).
decimalsOptional number of decimals applied to generated samples.
Method set_poisson_parameter()
Sets a parameter to be sampled from a Poisson distribution with lambda parameter. Produces integers.
Usage
LatinHypercubeSampler$set_poisson_parameter(parameter_name, lambda = 1)
Arguments
parameter_nameCharacter string name of sample parameter.
lambdaLambda parameter for the Poisson distribution. Must be positive (default = 1).
Method set_lognormal_parameter()
Sets a parameter to be sampled from a lognormal distribution with log mean and log standard deviation, optionally expressed as regular mean and SD (overriding log mean/sd), and optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_lognormal_parameter( parameter_name, meanlog = 0, sdlog = 1, mean = NULL, sd = NULL, decimals = NULL )
Arguments
parameter_nameCharacter string name of sample parameter.
meanlogLog mean parameter for the lognormal distribution (default = 0).
sdlogLog standard deviation parameter for the lognormal distribution (default = 1).
meanOptional (overriding) regular mean parameter for the lognormal distribution (default = NULL).
sdOptional (overriding) standard deviation parameter for the lognormal distribution (default = NULL).
decimalsOptional number of decimals applied to generated samples.
Method set_beta_parameter()
Sets a parameter to be sampled from a beta distribution configured with alpha and beta parameters, or optionally with mean and standard deviation (overriding alpha and beta), and optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_beta_parameter( parameter_name, alpha = 1, beta = 1, mean = NULL, sd = NULL, decimals = NULL )
Arguments
parameter_nameCharacter string name of sample parameter.
alphaShaping (towards 1) parameter (> 0) for the beta distribution (default = 1).
betaShaping (towards 0) parameter (> 0) for the beta distribution (default = 1).
meanOptional (overriding) mean parameter for the beta distribution (default = NULL).
sdOptional (overriding) standard deviation parameter for the beta distribution (default = NULL).
decimalsOptional number of decimals applied to generated samples.
Method set_truncnorm_parameter()
Sets a parameter to be sampled from a truncated normal distribution with mean, standard deviation, and lower and upper bounds, optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_truncnorm_parameter( parameter_name, mean = 0, sd = 1, lower = -Inf, upper = Inf, decimals = NULL )
Arguments
parameter_nameCharacter string name of sample parameter.
meanMean parameter of the truncated normal distribution (default = 0).
sdStandard deviation of the truncated normal distribution (default = 1).
lowerLower bound of the truncated normal distribution (default = -Inf, meaning no lower bound).
upperUpper bound of the truncated normal distribution (default = Inf, meaning no upper bound).
decimalsOptional number of decimals that generated samples are rounded to.
Method set_triangular_parameter()
Sets a parameter to be sampled from a triangular distribution with lower and upper bounds and mode (peak), optionally rounded to a specified number of decimal places.
Usage
LatinHypercubeSampler$set_triangular_parameter( parameter_name, lower = 0, upper = 1, mode = (lower + upper)/2, decimals = NULL )
Arguments
parameter_nameCharacter string name of sample parameter.
lowerLower bound of the triangular distribution (default = 0).
upperUpper bound of the triangular distribution (default = 1).
modeMode (or peak) of the triangular distribution (default = (lower + upper)/2).
decimalsOptional number of decimals applied to generated samples.
Method generate_samples()
Generates Latin hypercube sample data (via randomLHS) for the set parameters using corresponding distributions.
Usage
LatinHypercubeSampler$generate_samples(number = 10, random_seed = NULL)
Arguments
numberNumber of samples to generate (default = 10).
random_seedOptional seed for the random generation of samples.
Returns
A data frame of generated sample values.
Method clone()
The objects of this class are cloneable with this method.
Usage
LatinHypercubeSampler$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
lhs_gen <- LatinHypercubeSampler$new(parameter_names = c("size", "age", "km", "price"))
lhs_gen$set_class_parameter("size", c("small", "medium", "large"))
lhs_gen$set_uniform_parameter("age", lower = 18, upper = 70, decimals = 0)
lhs_gen$set_poisson_parameter("offspring", lambda = 2)
lhs_gen$set_normal_parameter("km", mean = 50000, sd = 20000, decimals = 0)
lhs_gen$set_truncnorm_parameter("kg", mean = 75, sd = 20, lower = 0, upper = Inf, decimals = 2)
lhs_gen$set_lognormal_parameter("price", mean = 30000, sd = 10000, decimals = 0)
lhs_gen$set_beta_parameter("tread", mean = 0.7, sd = 0.1, decimals = 2)
lhs_gen$set_triangular_parameter("rating",
lower = 0, upper = 10, mode = 5,
decimals = 1
)
lhs_gen$generate_samples(number = 10, random_seed = 123)
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