Create plots of parameters and criterion values per iteration or evaluation number

Create plots of parameters and criterion values per iteration or evaluation number

plot_valuesVSit(
  df,
  param_info,
  iter_or_eval = c("iter", "eval"),
  crit_log = TRUE,
  rep_label = c("begin_end", "begin", "end")
)

Arguments

df

Data.frame containing values of parameters (one column per estimated parameter), criterion (crit column), repetition number (rep), iteration number (iter) and evaluation number (eval) (similar to params_and_crit). See Details section for comments about the difference between evaluations and iterations.

param_info

Information on the parameters to estimate. Either a list containing:

• ub and lb, named vectors of upper and lower bounds (-Inf and Inf can be used if init_values is provided),

• init_values, a data.frame containing initial values to test for the parameters (optional, if not provided, or if less values than number of repetitions of the minimization are provided, the, or part of the, initial values will be randomly generated using LHS sampling within parameter bounds).

or a named list containing for each parameter:

• sit_list, list the groups of situations for which the current estimated parameter must take different values (see here for an example),

• ub and lb, vectors of upper and lower bounds (one value per group),

• init_values, the list of initial values per group (data.frame, one column per group, optional).

iter_or_eval

Values of the x axis: "iter" for iteration number, "eval" for evaluation number

crit_log

If TRUE, consider criterion values in log scale

rep_label

Indicate if labels for the repetition number must be plotted at both beginning and end of lines ("begin_end"), only at the beginning ("begin") or only at the end ("end")

Value

A named list containing one plot per parameter and a plot for the criterion.

Details

Evaluation means evaluation of the criterion from proposed values of the parameters by the parameter estimation algorithm. An iteration is reached when an evaluation lead to a better value of the criterion than the previously obtained values. There are thus more evaluations than iterations. The criterion decreases when iteration number increases while it is not the case when evaluation number increases.