R6 class to represent a Latin hypercube sampler.

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

attached: A list of dynamically attached attributes (name-value pairs).

Active bindings

parameter_names: A vector of sample parameter names.
parameter_distributions: A list of sample distribution values (nested list with appropriate parameters).

Methods

Public methods

LatinHypercubeSampler$new()
LatinHypercubeSampler$set_class_parameter()
LatinHypercubeSampler$set_uniform_parameter()
LatinHypercubeSampler$set_normal_parameter()
LatinHypercubeSampler$set_poisson_parameter()
LatinHypercubeSampler$set_lognormal_parameter()
LatinHypercubeSampler$set_beta_parameter()
LatinHypercubeSampler$set_truncnorm_parameter()
LatinHypercubeSampler$set_triangular_parameter()
LatinHypercubeSampler$generate_samples()
LatinHypercubeSampler$clone()

Inherited methods

poems::GenericClass$new_clone()

Method `new()`

Initialization method sets parameter names when provided.

Usage

LatinHypercubeSampler$new(parameter_names = NULL, ...)

Arguments

parameter_names: Optional 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_name: Character string name of sample parameter.
classes: Vector 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_name: Character string name of sample parameter.
lower: Lower bound of the uniform distribution (default = 0).
upper: Upper bound of the uniform distribution (default = 1).
decimals: Optional 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_name: Character string name of sample parameter.
mean: Mean parameter for the normal distribution (default = 0).
sd: Standard deviation parameter for the normal distribution (default = 1).
decimals: Optional 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_name: Character string name of sample parameter.
lambda: Lambda 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_name: Character string name of sample parameter.
meanlog: Log mean parameter for the lognormal distribution (default = 0).
sdlog: Log standard deviation parameter for the lognormal distribution (default = 1).
mean: Optional (overriding) regular mean parameter for the lognormal distribution (default = NULL).
sd: Optional (overriding) standard deviation parameter for the lognormal distribution (default = NULL).
decimals: Optional 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_name: Character string name of sample parameter.
alpha: Shaping (towards 1) parameter (> 0) for the beta distribution (default = 1).
beta: Shaping (towards 0) parameter (> 0) for the beta distribution (default = 1).
mean: Optional (overriding) mean parameter for the beta distribution (default = NULL).
sd: Optional (overriding) standard deviation parameter for the beta distribution (default = NULL).
decimals: Optional 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_name: Character string name of sample parameter.
mean: Mean parameter of the truncated normal distribution (default = 0).
sd: Standard deviation of the truncated normal distribution (default = 1).
lower: Lower bound of the truncated normal distribution (default = -Inf, meaning no lower bound).
upper: Upper bound of the truncated normal distribution (default = Inf, meaning no upper bound).
decimals: Optional 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_name: Character string name of sample parameter.
lower: Lower bound of the triangular distribution (default = 0).
upper: Upper bound of the triangular distribution (default = 1).
mode: Mode (or peak) of the triangular distribution (default = (lower + upper)/2).
decimals: Optional 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

number: Number of samples to generate (default = 10).
random_seed: Optional 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

deep: Whether 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)
#>      size age    km price offspring     kg tread rating
#> 1  medium  58 81395 27974         2  40.79  0.62    4.4
#> 2   small  40 67785 35994         0  68.14  0.66    8.0
#> 3   small  50 57527 48720         2  81.81  0.63    5.5
#> 4   large  64 66229 40466         2  75.74  0.91    7.1
#> 5  medium  28 37868 28792         3 127.10  0.72    5.8
#> 6   large  35 46512 22199         1  58.06  0.47    4.5
#> 7   small  32 30269 33404         1  73.68  0.81    6.6
#> 8  medium  48 54963 24981         0  87.93  0.73    2.4
#> 9   large  66 10421 19036         3  62.94  0.78    3.5
#> 10 medium  19 40146 16062         5  93.44  0.69    1.0

Super class

Public fields

Active bindings

Methods

Public methods

Method new()

Usage

Arguments

Method set_class_parameter()

Usage

Arguments

Method set_uniform_parameter()

Usage

Arguments

Method set_normal_parameter()

Usage

Arguments

Method set_poisson_parameter()

Usage

Arguments

Method set_lognormal_parameter()

Usage

Arguments

Method set_beta_parameter()

Usage

Arguments

Method set_truncnorm_parameter()

Usage

Arguments

Method set_triangular_parameter()

Usage

Arguments

Method generate_samples()

Usage

Arguments

Returns

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `set_class_parameter()`

Method `set_uniform_parameter()`

Method `set_normal_parameter()`

Method `set_poisson_parameter()`

Method `set_lognormal_parameter()`

Method `set_beta_parameter()`

Method `set_truncnorm_parameter()`

Method `set_triangular_parameter()`

Method `generate_samples()`

Method `clone()`