plot_evpi_threshold: Function to estimate the expected value of partial perfect...

In sheejamk/packEVPI: Estimate Expected Value of Perfect Information

Function to estimate the expected value of partial perfect information This is the outer loop of the two stage Monte Carlo process

Description

Function to estimate the expected value of partial perfect information This is the outer loop of the two stage Monte Carlo process

Usage

plot_evpi_threshold(result_evpi_evppi)

Arguments

result_evpi_evppi

result from estimation of evpi and evppi

Value

plot the plot

Examples

param_file <- system.file("extdata", "table_param.csv", package = "packEVPI")
well <- packDAMipd::health_state("well", cost = "cost_well_co", utility = 1)
disabled <- packDAMipd::health_state("disabled", cost = "cost_dis_co",
utility = "utility_dis_co")
dead <- packDAMipd::health_state("dead", cost = 0, utility = 0)
tmat <- rbind(c(1, 2, 3), c(NA, 4, 5), c(NA, NA, 6))
colnames(tmat) <- rownames(tmat) <- c("well", "disabled", "dead")
tm <- packDAMipd::populate_transition_matrix(3, tmat,
c("tp_well_well_co","tp_well_dis_co","tp_well_dead", "tp_dis_dis_co",
"tp_dis_dead", "tp_dead_dead"),colnames(tmat))
health_states <- packDAMipd::combine_state(well, disabled, dead)
this.strategy <- packDAMipd::strategy(tm, health_states, "control")
param_list <- packDAMipd::define_parameters(
tp_well_dis_co = packDAMipd::get_parameter_read("tp_well_dis_co",
param_file),
tp_well_dis_in =  packDAMipd::get_parameter_read("tp_well_dis_in",
param_file),
tp_well_dead =  packDAMipd::get_parameter_read("tp_well_dead", param_file),
tp_dis_dead =  packDAMipd::get_parameter_read("tp_dis_dead", param_file),
tp_dead_dead =  1,
cost_well_co =  packDAMipd::get_parameter_read("cost_well_co", param_file),
cost_well_in =  packDAMipd::get_parameter_read("cost_well_in", param_file),
cost_dis_co =  packDAMipd::get_parameter_read("cost_dis_co", param_file),
cost_dis_in =  packDAMipd::get_parameter_read("cost_dis_in", param_file),
utility_dis_co =  packDAMipd::get_parameter_read("utility_dis_co",
param_file),
utility_dis_in =  packDAMipd::get_parameter_read("utility_dis_in",
param_file),
tp_well_well_co = "1-(tp_well_dis_co + tp_well_dead)",
tp_well_well_in = "1-(tp_well_dis_in + tp_well_dead)",
tp_dis_dis_co = "1-( tp_dis_dead)",
tp_dis_dis_in = "1-( tp_dis_dead)")
this_markov <- packDAMipd::markov_model(this.strategy, 24, c(1000, 0, 0),
discount = c(0, 0), method = "half cycle correction", param_list)
well <- packDAMipd::health_state("well", cost = "cost_well_in", utility = 1)
disabled <- packDAMipd::health_state("disabled", cost = "cost_dis_in",
utility = "utility_dis_in")
dead <- packDAMipd::health_state("dead", cost = 0, utility = 0)
tmat <- rbind(c(1, 2, 3), c(NA, 4, 5), c(NA, NA, 6))
colnames(tmat) <- rownames(tmat) <- c("well", "disabled", "dead")
tm <- packDAMipd::populate_transition_matrix(3, tmat, c("tp_well_well_in",
 "tp_well_dis_in","tp_well_dead", "tp_dis_dis_in","tp_dis_dead", "tp_dead_dead"),
 colnames(tmat))
 health_states <- packDAMipd::combine_state(well, disabled, dead)
 this.strategy <- packDAMipd::strategy(tm, health_states, "intervention")
 sec_markov <- packDAMipd::markov_model(this.strategy, 24, c(1000, 0, 0),
 discount = c(0, 0),method = "half cycle correction", param_list)
 list_markov <- packDAMipd::combine_markov(list(this_markov, sec_markov))
 parameter_of_interest <- "tp_well_dis_co"
 colnames_paramdistr  <- c("Param1_name", "Param1_value", "Param2_name",
  "Param2_value")
  threshold_values <- c(5000, 10000, 150000, 20000)
  res <- estimate_evpi_evppi_diff_threshold(parameter_of_interest, param_file,
  colnames_paramdistr, list_markov, threshold_values, outer_iterations = 3,
  inner_iterations = 5)
  plot_evpi_threshold(res)

sheejamk/packEVPI documentation built on April 7, 2023, 8:48 a.m.