In cboettig/multiple_uncertainty: Multiple Uncertainty

library("dplyr")
library("tidyr")
library("ggplot2")
library("multipleuncertainty")
library("parallel")
knitr::opts_chunk$set(cache = TRUE)

Robustness to noise level

We consider the results across a range of possible values for the "low" and "high" noise limits chosen.

fig3 <- function(low, high, noise_dist){  
  grid <- seq(0, 200, length = 401)
  model <- "logistic"
  small     <- multiple_uncertainty(f = model, x_grid = grid, sigma_g = low, sigma_m = low, sigma_i = low, noise_dist = noise_dist)
  growth    <- multiple_uncertainty(f = model, x_grid = grid, sigma_g = high, sigma_m = low, sigma_i = low, noise_dist = noise_dist)
  measure   <- multiple_uncertainty(f = model, x_grid = grid, sigma_g = low, sigma_m = high, sigma_i = low, noise_dist = noise_dist)
  implement <- multiple_uncertainty(f = model, x_grid = grid, sigma_g = low, sigma_m = low, sigma_i = high, noise_dist = noise_dist)
  ## Combine records by scenario
  df <- data.frame(y_grid = grid, small = small, growth = growth, 
                   measure = measure, implement = implement) %>%
    tidyr::gather(scenario, value, -y_grid)
}

expand.grid(low = c(0, 0.01, 0.1), high = c(0.2, 0.3, 0.4, 0.5, 0.8, 0.9), noise_dist = c("lognormal", "uniform")) %>%
  dplyr::group_by(low, high, noise_dist) %>%
  dplyr::do(fig3(.$low, .$high, .$noise_dist)) -> df

df %>% filter(noise_dist == "lognormal") %>%
  ggplot(aes(x = y_grid, y = value, col = scenario)) + 
    geom_line()  + 
    facet_grid(high ~ low) + 
    xlab("Stock") + 
    ylab("Escapement") + 
    coord_cartesian(xlim = c(0, 150), ylim = c(0,100)) + 
    theme_bw() + 
    ggtitle("Lognormal noise")

df %>% filter(noise_dist == "uniform") %>%
  ggplot(aes(x = y_grid, y = value, col = scenario)) + 
    geom_line()  + 
    facet_grid(high ~ low) + 
    xlab("Stock") + 
    ylab("Escapement") + 
    coord_cartesian(xlim = c(0, 150), ylim = c(0,100)) + 
    theme_bw() + 
    ggtitle("Uniform noise")