`variable.Rd`

`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()`

.

variable(lower = -Inf, upper = Inf, dim = NULL) cholesky_variable(dim, correlation = FALSE) simplex_variable(dim) ordered_variable(dim)

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 |
---|---|

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). |

`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.

# NOT RUN { # 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))