optimizeR (version 1.2.0)

ParameterSpaces: Switch between parameter spaces

Description

The ParameterSpaces object manages two related parameter spaces:

• the Optimization Space (for optimization)

• and the Interpretation Space (for easier interpretation).

In the Optimization Space, parameters are stored as a numeric vector, the standard format for numerical optimizers.

In the Interpretation Space, parameters are stored as a list and can take different formats (e.g., matrix).

The user can define transformation functions (not necessarily bijective) to switch between these spaces via the $o2i() and $i2o() methods.

Methods

Public methods

• ParameterSpaces$new()

• ParameterSpaces$print()

• ParameterSpaces$switch()

• ParameterSpaces$o2i()

• ParameterSpaces$i2o()

• ParameterSpaces$clone()

---

Method new()

Initializes a new ParameterSpaces object.

Usage

ParameterSpaces$new(parameter_names, parameter_lengths_in_o_space)

Arguments

parameter_names

[character()]
Unique names for the parameters.

parameter_lengths_in_o_space

[integer()]
The length of each parameter in the optimization space.

Returns

A new ParameterSpaces object.

---

Method print()

Print an overview of the parameter spaces.

Usage

ParameterSpaces$print(show_transformer = FALSE)

Arguments

show_transformer

[logical(1)]
Show transformer functions in the output?

---

Method switch()

Switch between Optimization Space and Interpretation Space.

Usage

ParameterSpaces$switch(x, to = NULL)

Arguments

x

[numeric() | list()]
The parameters, either as a numeric vector (will be switched to Interpretation Space), or as a list() (will be switched to Optimization Space).

to

[character(1) | NULL]
Explicitly switch to a specific space, either

• "o": Optimization Space

• "i": Interpretation Space

If NULL, the function will switch to the other space.

---

Method o2i()

Define transformation functions when switching from Optimization Space to Interpretation Space.

Usage

ParameterSpaces$o2i(...)

Arguments

...

[function]
One or more transformation functions, named according to the parameters.

Transformers from Optimization Space to Interpretation Space (o2i) must receive a numeric. The default is the identity.

---

Method i2o()

Define transformation functions when switching from Interpretation Space to Optimization Space.

Usage

ParameterSpaces$i2o(...)

Arguments

...

[function]
One or more transformers functions, named according to the parameters.

Transformers from Interpretation Space to Optimization Space (i2o) must return a numeric. The default is as.vector().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ParameterSpaces$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

### Log-likelihood function of two-class Gaussian mixture model with
### parameter vector `theta` that consists of
### - `mu`, mean vector of length 2
### - `sd`, standard deviation vector of length 2, must be positive
### - `lambda`, class probability of length 1, must be between 0 and 1

normal_mixture_llk <- function(theta, data) {
  mu <- theta[1:2]
  sd <- exp(theta[3:4])
  lambda <- plogis(theta[5])
  c1 <- lambda * dnorm(data, mu[1], sd[1])
  c2 <- (1 - lambda) * dnorm(data, mu[2], sd[2])
  sum(log(c1 + c2))
}

### define parameter spaces
### - `mu` needs no transformation
### - `sd` needs to be real in optimization space and positive in
###    interpretation space
### - `lambda` needs to be real and of length `1` in optimization space, and
###    a probability vector of length `2` in interpretation space

normal_mixture_spaces <- ParameterSpaces$
  new(
    parameter_names = c("mu", "sd", "lambda"),
    parameter_lengths_in_o_space = c(2, 2, 1)
  )$
  o2i(
    "mu" = function(x) x,
    "sd" = function(x) exp(x),
    "lambda" = function(x) c(plogis(x), 1 - plogis(x))
  )$
  i2o(
    "mu" = function(x) x,
    "sd" = function(x) log(x),
    "lambda" = function(x) qlogis(x[1])
  )

### switch between parameter spaces

par <- list(                             # parameters in interpretation space
  "mu" = c(2, 4),
  "sd" = c(0.5, 1),
  "lambda" = c(0.4, 0.6)
)
(x <- normal_mixture_spaces$switch(par)) # switch to optimization space
normal_mixture_llk(
  theta = x, data = datasets::faithful$eruptions
)
normal_mixture_spaces$switch(x)          # switch back