library("dplyr") library("tidyr") library("ggplot2") library("multipleuncertainty")
fig3 <- function(noise, assume){ grid <- seq(0, 200, by=0.5) small <- multiple_uncertainty(f = logistic, x_grid = grid, sigma_g = 0.1, sigma_m = 0.1, sigma_i = 0.1, noise_dist = noise, assume = assume) growth <- multiple_uncertainty(f = logistic, x_grid = grid, sigma_g = 0.5, sigma_m = 0.1, sigma_i = 0.1, noise_dist = noise, assume = assume) measure <- multiple_uncertainty(f = logistic, x_grid = grid, sigma_g = 0.1, sigma_m = 0.5, sigma_i = 0.1, noise_dist = noise, assume = assume) implement <- multiple_uncertainty(f = logistic, x_grid = grid, sigma_g = 0.1, sigma_m = 0.1, sigma_i = 0.5, noise_dist = noise, assume = assume) df <- data.frame(y_grid = grid, small = small, growth = growth, measure = measure, implement = implement) %>% tidyr::gather(scenario, value, -y_grid) } df <- expand.grid(noise = c("uniform", "lognormal"), assume = c("Bayes", "Reciprocal")) %>% dplyr::group_by(noise, assume) %>% dplyr::do(fig3(.$noise, .$assume))
df %>% ggplot(aes(x = y_grid, y = value, col = scenario)) + geom_line() + facet_grid(assume ~ noise) + xlab("Stock") + ylab("Escapement") + coord_cartesian(xlim = c(0, 150), ylim = c(0,100)) + theme_bw()
reed <- multiple_uncertainty(noise_dist = "lognormal") qplot(seq_along(reed), reed) + coord_cartesian(xlim = c(0, 150), ylim = c(0, 150)) + xlab("Stock") + ylab("Escapement") + theme_bw()
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