Plot the prior distribution for a simmr run

This function takes the output from simmr_mcmc or simmr_ffvb and plots the prior distribution to enable visual inspection. This can be used by itself or together with posterior_predictive to visually evaluate the influence of the prior on the posterior distribution.

prior_viz(
  simmr_out,
  group = 1,
  plot = TRUE,
  include_posterior = TRUE,
  n_sims = 10000,
  scales = "free"
)

Arguments

simmr_out

A run of the simmr model from simmr_mcmc or simmr_ffvb

group

Which group to run it for (currently only numeric rather than group names)

plot

Whether to create a density plot of the prior or not. The simulated prior values are returned as part of the object

include_posterior

Whether to include the posterior distribution on top of the priors. Defaults to TRUE

n_sims

The number of simulations from the prior distribution

scales

The type of scale from facet_wrap allowing for fixed, free, free_x, free_y

Value

A list containing plot: the ggplot object (useful if requires customisation), and sim: the simulated prior values which can be compared with the posterior densities

Examples

# \donttest{
data(geese_data_day1)
simmr_1 <- with(
  geese_data_day1,
  simmr_load(
    mixtures = mixtures,
    source_names = source_names,
    source_means = source_means,
    source_sds = source_sds,
    correction_means = correction_means,
    correction_sds = correction_sds,
    concentration_means = concentration_means
  )
)

# Plot
plot(simmr_1)


# Print
simmr_1
#> This is a valid simmr input object with 
#> 9  observations, 
#> 2 tracers, and 
#> 4 sources.
#> The source names are: 
#> [1] "Zostera"      "Grass"        "U.lactuca"    "Enteromorpha"
#> .
#> The tracer names are: 
#> [1] "d13C_Pl" "d15N_Pl"
#> 
#> 

# MCMC run
simmr_1_out <- simmr_mcmc(simmr_1)
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 18
#>    Unobserved stochastic nodes: 6
#>    Total graph size: 136
#> 
#> Initializing model
#> 

# Prior predictive
prior <- prior_viz(simmr_1_out)

head(prior$p_prior_sim)
#>         Zostera      Grass  U.lactuca Enteromorpha
#> [1,] 0.20070423 0.18363495 0.53669789   0.07896293
#> [2,] 0.61953581 0.08033049 0.03936815   0.26076554
#> [3,] 0.51473498 0.04340801 0.14772533   0.29413168
#> [4,] 0.16616109 0.33314262 0.43534939   0.06534690
#> [5,] 0.06308184 0.72766333 0.05886188   0.15039295
#> [6,] 0.22535115 0.09985581 0.49918481   0.17560824
summary(prior$p_prior_sim)
#>     Zostera            Grass            U.lactuca         Enteromorpha     
#>  Min.   :0.00193   Min.   :0.003437   Min.   :0.003398   Min.   :0.001426  
#>  1st Qu.:0.09887   1st Qu.:0.100335   1st Qu.:0.101007   1st Qu.:0.099005  
#>  Median :0.19787   Median :0.199266   Median :0.202569   Median :0.199167  
#>  Mean   :0.24899   Mean   :0.251589   Mean   :0.249707   Mean   :0.249713  
#>  3rd Qu.:0.35415   3rd Qu.:0.360822   3rd Qu.:0.355151   3rd Qu.:0.356805  
#>  Max.   :0.94942   Max.   :0.944411   Max.   :0.919671   Max.   :0.965063  
# }