water-grid-thing.md

library(magrittr)
compute_posterior_water_grid <- function(n = 20,
                                         n_water = 6,
                                         n_land = 3,
                                         prior = rep(x = 1,times = n),
                                         p_grid = seq(from = 0, 
                                                      to = 1, length.out = n)){
  
  # define posterior
  # p_grid <- seq(from = 0, to = 1, length.out = n)
  # compute likelihood at each value in grid
  message("printing p_grid")
  print(p_grid)
  
  message("printing prior")
  print(prior)
  likelihood <- dbinom(x = n_water, size = n_water + n_land, prob = p_grid)
  
  # compute product of likelihood and prior
  unstd_posterior <- likelihood * prior
  
  # standardize the posterior, so it sums to 1
  posterior <- unstd_posterior / sum(unstd_posterior)
  
  tibble::tibble(grid = p_grid, post = posterior)
  
}

base_plot_posterior_water_grid <- function(data){
  with(data,
       plot(grid,
            post,
            type = "b",
            xlab = "Probability of Water",
            ylab = "Posterior probability"))
  
  mtext(paste0(nrow(data), " points"))
}


water_three <- compute_posterior_water_grid(n_water = 3,
                                            n_land = 0)
#> printing p_grid
#>  [1] 0.00000000 0.05263158 0.10526316 0.15789474 0.21052632 0.26315789
#>  [7] 0.31578947 0.36842105 0.42105263 0.47368421 0.52631579 0.57894737
#> [13] 0.63157895 0.68421053 0.73684211 0.78947368 0.84210526 0.89473684
#> [19] 0.94736842 1.00000000
#> printing prior
#>  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
base_plot_posterior_water_grid(water_three)

compute_posterior_water_grid(n_water = 3,
                             n_land = 1) %>% 
  base_plot_posterior_water_grid()
#> printing p_grid
#>  [1] 0.00000000 0.05263158 0.10526316 0.15789474 0.21052632 0.26315789
#>  [7] 0.31578947 0.36842105 0.42105263 0.47368421 0.52631579 0.57894737
#> [13] 0.63157895 0.68421053 0.73684211 0.78947368 0.84210526 0.89473684
#> [19] 0.94736842 1.00000000
#> printing prior
#>  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

compute_posterior_water_grid(n_water = 5,
                             n_land = 2) %>% 
  base_plot_posterior_water_grid()
#> printing p_grid
#>  [1] 0.00000000 0.05263158 0.10526316 0.15789474 0.21052632 0.26315789
#>  [7] 0.31578947 0.36842105 0.42105263 0.47368421 0.52631579 0.57894737
#> [13] 0.63157895 0.68421053 0.73684211 0.78947368 0.84210526 0.89473684
#> [19] 0.94736842 1.00000000
#> printing prior
#>  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

####

# changing the prior to be 0 when below 0.5, and 1 otherwise
compute_posterior_water_grid(prior = ifelse(p_grid < 0.5, 0, 1),
                             n_water = 3,
                             n_land = 1)
#> printing p_grid
#>  [1] 0.00000000 0.05263158 0.10526316 0.15789474 0.21052632 0.26315789
#>  [7] 0.31578947 0.36842105 0.42105263 0.47368421 0.52631579 0.57894737
#> [13] 0.63157895 0.68421053 0.73684211 0.78947368 0.84210526 0.89473684
#> [19] 0.94736842 1.00000000
#> printing prior
#> Error in ifelse(p_grid < 0.5, 0, 1): object 'p_grid' not found

# but this doesn't work?!?!

^{Created on 2020-10-12 by the reprex package (v0.3.0)}

I think it's an issue with environments. Here's a simple example:

foo <- function(arg) {

  things <- c(1,2,3)

  print(arg)

}

foo(things <= 2)
#> Error in print(arg): object 'things' not found

^{Created on 2020-10-12 by the reprex package (v0.3.0)}

The expression for arg is not executed in the function's execution environment. It is executed in the calling environment. There's a chapter in Adv R on this, but I actually find it more confusing than helpful, your mileage may vary: http://adv-r.had.co.nz/Environments.html#function-envs

hmmm, random thought, can I pass ... and use those instead? It would require a bit of control statements to determine if the dots are set and to update the objects.