Plot a Subset of Parameters From a LiBBi Model

Plot either the distribution or trace of a subset of parameters split out by their dimension

plot_param(libbi_model = NULL, param = NULL, prior_params = NULL,
  log_scale = FALSE, sqrt_scale = FALSE, plot_type = "dist",
  plot_data = TRUE, burn_in = 0, scales = "free")

Arguments

libbi_model	LiBBi model object
param	A character vector containing the complete names of the variables to plot.
prior_params	A prior parameter set as generated by fit_model. Alternatively a libbi model can be passed.
log_scale	Logical, defaults to TRUE. Should the x axis be plotted on a log scale?
sqrt_scale	Logical, defaults to TRUE. Should the x axis be plotted on a square root scale?
plot_type	A character string indicating the type of plot to show. "dist" plots a density and rug plot, whilst "trace" plots a trace plot.
plot_data	Logical, defaults to TRUE. Should the summarised data be plotted.
burn_in	Numeric, defaults to 0. The number of samples to remove as burn in.
scales	Character string, defaulting to "free". What scales to use for graph faceting.

Value

A plot of the specified parameters.

Examples

##Show function code
plot_param
#> function(libbi_model = NULL, param = NULL,
#>                        prior_params = NULL, log_scale = FALSE, sqrt_scale = FALSE, 
#>                        plot_type = "dist", plot_data = TRUE, burn_in = 0, scales = "free") {
#>   
#>     
#>     data <- bi_read(libbi_model, type = c("param")) %>% 
#>       map_dfr(~mutate(., distribution = "Posterior"), .id = "parameter")
#>     
#>     if (!is.null(param)) {
#>       data <- data %>% 
#>       filter(parameter %in% param)
#>     }
#>     
#>     if (!is.null(prior_params)) {
#>       
#>       if (class(prior_params) != "libbi") {
#>         prior <- prior_params %>% 
#>           mutate(distribution = "Prior")
#>       }else {
#>         prior <- bi_read(prior_params, type = c("param")) %>% 
#>           map_dfr(~mutate(., distribution = "Prior"), .id = "parameter")
#>       }
#>       
#>       if (!is.null(param)) {
#>         prior <- prior %>% 
#>           filter(parameter %in% param)
#>       }
#> 
#>       data <- suppressWarnings(data %>% 
#>         bind_rows(prior))
#>     }
#>     
#>     data <- data %>% 
#>       group_by(np, distribution, parameter) %>% 
#>       mutate(length = 1:n()) %>% 
#>       rename(Distribution = distribution) %>% 
#>       ungroup %>% 
#>       filter(any(np > burn_in, Distribution %in% "Prior"))
#>     
#>     if (plot_data) {
#>       if (plot_type == "dist") {
#>         plot <- ggplot(data, aes(x = value, col = Distribution, fill = Distribution)) +
#>           geom_density(alpha = 0.4) +
#>           geom_rug(alpha = 0.1) +
#>           labs(x = "Value", y = "Density")
#>       }else if (plot_type =="trace") {
#>         plot <- ggplot(data, aes(x = np, y = value, col = Distribution)) +
#>           geom_line(alpha = 0.8) +
#>           labs(x = "Time", y = "Value")
#>       }
#>       
#>       plot <- plot +
#>         facet_wrap(c("parameter", "length"), scales = scales) +
#>         theme_minimal() +
#>         theme(legend.position = "top")
#>       
#>       if (log_scale) {
#>         plot <- plot +
#>           scale_x_log10(labels = comma)
#>       }
#>       
#>       if (sqrt_scale) {
#>         plot <- plot +
#>           scale_x_sqrt(labels = comma)
#>       }
#>     }else{
#>       plot <- data
#>     }
#>     
#>  return(plot)
#> }
#> <environment: namespace:ModelTBBCGEngland>