pick_val_v | R Documentation |
Select which values should be applied in the corresponding loop for several values (vector or list).
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
)
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) |
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
List used for the inputs
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))
)
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