create greta variables

variable() creates greta arrays representing unknown parameters, to be learned during model fitting. These parameters are not associated with a probability distribution. To create a variable greta array following a specific probability distribution, see distributions().

Usage

variable(lower = -Inf, upper = Inf, dim = NULL)

cholesky_variable(dim, correlation = FALSE)

simplex_variable(dim)

ordered_variable(dim)

Arguments

lower, upper

optional limits to variables. These must be specified as numerics, they cannot be greta arrays (though see details for a workaround). They can be set to -Inf (lower) or Inf (upper), though lower must always be less than upper.

dim

the dimensions of the greta array to be returned, either a scalar or a vector of positive integers. See details.

correlation

whether to return a cholesky factor corresponding to a correlation matrix (diagonal elements equalling 1, off-diagonal elements between -1 and 1).

Details

lower and upper must be fixed, they cannot be greta arrays. This ensures these values can always be transformed to a continuous scale to run the samplers efficiently. However, a variable parameter with dynamic limits can always be created by first defining a variable constrained between 0 and 1, and then transforming it to the required scale. See below for an example.

The constraints in simplex_variable() and ordered_variable() operate on the final dimension, which must have more than 1 element. Passing in a scalar value for dim therefore results in a row-vector.

Examples

if (FALSE) { # \dontrun{

# a scalar variable
a <- variable()

# a positive length-three variable
b <- variable(lower = 0, dim = 3)

# a 2x2x2 variable bounded between 0 and 1
c <- variable(lower = 0, upper = 1, dim = c(2, 2, 2))

# create a variable, with lower and upper defined by greta arrays
min <- as_data(iris$Sepal.Length)
max <- min^2
d <- min + variable(0, 1, dim = nrow(iris)) * (max - min)
} # }
# 4x4 cholesky factor variables for covariance and correlation matrices
e_cov <- cholesky_variable(dim = 4)
e_correl <- cholesky_variable(dim = 4, correlation = TRUE)

# these can be converted to symmetic matrices with chol2symm
# (equivalent to t(e_cov) %*% e_cov, but more efficient)
cov <- chol2symm(e_cov)
correl <- chol2symm(e_correl)
# a 4D simplex (sums to 1, all values positive)
f <- simplex_variable(4)

# a 4D simplex on the final dimension
g <- simplex_variable(dim = c(2, 3, 4))
# a 2D variable with each element higher than the one in the cell to the left
h <- ordered_variable(dim = c(3, 4))

# more constraints can be added with monotonic transformations, e.g. an
# ordered positive variable
i <- exp(ordered_variable(5))