Sample continuous data

cont_sample_data() samples data based on expert estimates stored in the elic_cont object.

Usage

cont_sample_data(
  x,
  round,
  ...,
  method = "basic",
  var = "all",
  n_votes = 1000,
  weights = 1,
  verbose = TRUE
)

Arguments

x: an object of class elic_cont.
round: integer indicating if the data belongs to the first or second elicitation round.
...: Unused arguments, included only for future extensions of the function.
method: character string with the name of the method to sample the data, only the basic is implemented, see Method below.
var: character string with the name of the variable or character vector with more variable names that you want to extract from the data. Use all for all variables.
n_votes: numeric indicating the number of votes to consider.
weights: numeric vector with the weights to apply to the estimates. If equal to 1, each experts get n_votes votes, see Weights below.
verbose: logical, if TRUE it prints informative messages.

Value

A tibble with the sampled data. This object has an additional class cont_sample used to implement the plotting method.

Weights

To provide a different number of votes to each expert, use the weights argument. The length of the vector must be equal to the number of experts. If provided when the elicitation type is the four points elicitation, their values overwrite the confidence estimates.

Method

The function samples the data using the basic method. The basic method samples the data based on the expert estimates with differences between the different elicitation types:

one point elicitation: the best estimate of each expert represent the pool of values that are sampled n_votes * n_experts times, with repetition.
three points elicitation: the minimum, best, and maximum estimates of each expert are used as scaling parameters of the PERT distribution from which the data are sampled. The weights argument can be used to weight the estimates of each expert.
five points elicitation: the minimum, best, and maximum estimates of each expert are rescaled according to their confidence and used as scaling parameters of the PERT distribution from which the data are sampled.

Author

Sergio Vignali

Examples

# Create the elict object and add data for the first and second round from a
# data.frame.
my_elicit <- cont_start(var_names = c("var1", "var2", "var3"),
                        var_types = "ZNp",
                        elic_types = "134",
                        experts = 6) |>
  cont_add_data(x, data_source = round_1, round = 1) |>
  cont_add_data(data_source = round_2, round = 2)
#> ✔ <elic_cont> object for "Elicitation" correctly initialised
#> ✔ Data added to "Round 1" from "data.frame"
#> ✔ Data added to "Round 2" from "data.frame"

# Sample data for the second round for all variables
samp <- cont_sample_data(my_elicit, round = 2)
#> ✔ Rescaled min and max for variable "var3".
#> ✔ Data for "var1", "var2", and "var3" sampled successfully using the "basic" method.
samp
#> # A tibble: 18,000 × 3
#>    id      var   value
#>    <chr>   <chr> <dbl>
#>  1 5ac97e0 var1      0
#>  2 5ac97e0 var1      0
#>  3 5ac97e0 var1     -4
#>  4 5ac97e0 var1      1
#>  5 5ac97e0 var1      1
#>  6 5ac97e0 var1     -4
#>  7 5ac97e0 var1      1
#>  8 5ac97e0 var1     -2
#>  9 5ac97e0 var1     -2
#> 10 5ac97e0 var1      1
#> # ℹ 17,990 more rows

# Sample data for the first round for the variable `var1` and `var2`
samp <- cont_sample_data(my_elicit, round = 1, var = c("var1", "var2"))
#> ✔ Data for "var1" and "var2" sampled successfully using the "basic" method.

# Sample data for the second round for the variable `var3`. Notice that the
# data are rescaled using the expert confidence before sampling.
samp <- cont_sample_data(my_elicit, round = 2, var = "var1")
#> ✔ Data for "var1" sampled successfully using the "basic" method.

# Sample data for the first round for the variable `var3` providing the
# weights. Notice that the weights overwrite the confidence estimates and
# that some values are constrained to be between 0 and 1 after rescaling.
samp <- cont_sample_data(my_elicit, round = 1, var = "var3",
                         weights = c(0.8, 0.7, 0.9, 0.7, 0.6, 0.9))
#> ℹ Provided weights used instead of confidence estimates
#> Warning: ! Some values have been constrained to be between 0 and 1.
#> ✔ Rescaled min and max for variable "var3".
#> ✔ Data for "var3" sampled successfully using the "basic" method.