Select which values should be applied in the corresponding loop for several values (vector or list).

Usage

pick_val_v(
  base,
  psa,
  sens,
  psa_ind = psa_bool,
  sens_ind = sens_bool,
  indicator,
  indicator_psa = NULL,
  names_out = NULL,
  indicator_sens_binary = TRUE,
  sens_iterator = NULL,
  distributions = NULL,
  covariances = NULL
)

Arguments

base: Value if no PSA/DSA/Scenario
psa: Value if PSA
sens: Value if DSA/Scenario
psa_ind: Boolean whether PSA is active
sens_ind: Boolean whether Scenario/DSA is active
indicator: Indicator which checks whether the specific parameter/parameters is/are active in the DSA or Scenario loop
indicator_psa: Indicator which checks whether the specific parameter/parameters is/are active in the PSA loop. If NULL, it's assumed to be a vector of 1s of length equal to length(indicator)
names_out: Names to give the output list
indicator_sens_binary: Boolean, TRUE if parameters will be varied fully, FALSE if some elements of the parameters may be changed but not all
sens_iterator: Current iterator number of the DSA/scenario being run, e.g., 5 if it corresponds to the 5th DSA parameter being changed
distributions: List with length equal to length of base where the distributions are stored
covariances: List with length equal to length of base where the variance/covariances are stored (only relevant if multivariate normal are being used)

Value

List used for the inputs

Details

This function can be used with vectors or lists, but will always return a list. Lists should be used when correlated variables are introduced to make sure the selector knows how to choose among those This function allows to choose between using an approach where only the full parameters are varied, and an approach where subelements of the parameters can be changed

Examples

pick_val_v(base = list(0,0),
             psa =list(rnorm(1,0,0.1),rnorm(1,0,0.1)),
             sens = list(2,3),
             psa_ind = FALSE,
             sens_ind = TRUE,
             indicator=list(1,2),
             indicator_sens_binary = FALSE,
             sens_iterator = 2,
             distributions = list("rnorm","rnorm")
)
#> [[1]]
#> [1] 0
#> 
#> [[2]]
#> [1] 3
#> 

pick_val_v(base = list(2,3,c(1,2)),
             psa =sapply(1:3,
                         function(x) eval(call(
                           c("rnorm","rnorm","mvrnorm")[[x]],
                           1,
                           c(2,3,list(c(1,2)))[[x]],
                           c(0.1,0.1,list(matrix(c(1,0.1,0.1,1),2,2)))[[x]]
                         ))),
             sens = list(4,5,c(1.3,2.3)),
             psa_ind = FALSE,
             sens_ind = TRUE,
             indicator=list(1,2,c(3,4)),
             names_out=c("util","util2","correlated_vector") ,
             indicator_sens_binary = FALSE,
             sens_iterator = 4,
             distributions = list("rnorm","rnorm","mvrnorm"),
             covariances = list(0.1,0.1,matrix(c(1,0.1,0.1,1),2,2))
)
#> $util
#> [1] 2
#> 
#> $util2
#> [1] 3
#> 
#> $correlated_vector
#> [1] 1.03 2.30
#>