WARDEN (version 0.99.1)

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

Description

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
)

Value

List used for the inputs

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)

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")
)

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))
)