Random number generation expressions are used to randomly sample model parameters from suitable distributions for probabilistic sensitivity analysis. These functions are typically used when evaluating an object of class model_def defined using define_model().

define_rng(expr, n = 1, ...)

eval_rng(x, params = NULL, check = FALSE)

## Arguments

expr An expression used to randomly draw variates for each parameter of interest in the model. Braces should be used so that the result of the last expression within the braces is evaluated. The expression must return a list where each element is either a vector, matrix, data.frame, or data.table. The length of the vector and number of rows in the matrix/data.frame/data.table, must either be 1 or n. Number of samples of the parameters to draw. Additional arguments to pass to the environment used to evaluate expr. An object of class rng_def created with define_rng(). A list containing the values of parameters for random number generation. Each element of the list should either be a vector, matrix, data.frame, or data.table Whether to check the returned output so that (i) it returns a list and (ii) each element has the correct length or number of rows. Default is FALSE, meaning that any output can be returned. This is always TRUE when used inside define_model().

## Value

define_rng() returns an object of class rng_def, which is a list containing the unevaluated random number generation expressions passed to expr, n, and any additional arguments passed to ... . eval_rng() evaluates the rng_def object and should return a list.

## Details

hesim contains a number of random number generation functions that return parameter samples in convenient formats and do not require the number of samples, n, as arguments (see rng_distributions). The random number generation expressions are evaluated using eval_rng() and used within expr in define_rng(). If multivariate object is returned by eval_rng(), then the rows are random samples and columns are distinct parameters (e.g., costs for each health state, elements of a transition probability matrix).

rng_distributions, define_model(), define_tparams()

## Examples


params <- list(
alpha = matrix(c(75, 25, 33, 67), byrow = TRUE, ncol = 2),
inptcost_mean = c(A = 900, B = 1500, C = 2000),
outptcost_mean = matrix(c(300, 600, 800,
400, 700, 700),
ncol = 3, byrow = TRUE)
)
rng_def <- define_rng({
aecost_mean <- c(500, 800, 1000) # Local object not
# not returned by eval_rng()
list( # Sampled values of parameters returned by eval_rng()
p = dirichlet_rng(alpha), # Default column names
inptcost = gamma_rng(mean = inptcost_mean, # Column names based on
sd = inptcost_mean),  # named vector
outptcost = outptcost_mean, # No column names because
# outptcost_mean has none.
aecost = gamma_rng(mean = aecost_mean, # Explicit naming of columns
sd = aecost_mean,
names = aecost_colnames)
)
}, n = 2, aecost_colnames = c("A", "B", "C")) # Add aecost_colnames to environment
eval_rng(x = rng_def, params)
#> $p #> s1_s1 s1_s2 s2_s1 s2_s2 #> 1: 0.7641322 0.2358678 0.3216384 0.6783616 #> 2: 0.7717676 0.2282324 0.3332963 0.6667037 #> #>$inptcost
#>            A        B         C
#> 1:  233.0313 2524.334 1931.8009
#> 2: 1374.0879 2499.035  134.1758
#>
#> $outptcost #> [,1] [,2] [,3] #> [1,] 300 600 800 #> [2,] 400 700 700 #> #>$aecost
#>           A        B         C
#> 1: 883.7757 2086.364  855.9896
#> 2: 623.5611 1062.843 1898.9403
#>