plot_levels_hier_exp_4: Plot kernel density of samples in hierarchical tree

In rchan26/exp4FusionRCPP: Hierarchical and Sequential Monte Carlo Fusion for exp(-(x^4)/2)

Description

This function plots the kernel densities of the samples in the hierarchical tree (in red) and the tempered target densities that they were targetting (in blue)

Usage

plot_levels_hier_exp_4(hier_result, from, to, plot_rows, plot_columns,
  whole = F, bw = rep("nrd0", length(hier_result$samples)))

Arguments

hier_result	hierarchical Monte Carlo fusion result
from, to	the range over which the function will be plotted: integer (if want to have it constant throughout) or vector of length L, where L is the number of levels in the hierarchy
plot_rows	number of rows in plot (should make is so plot_rows x plot_columns = L)
plot_columns	number of rows in plot (should make is so plot_rows x plot_columns = L)
whole	boolean value: defaults to F, determines whether or not to plot kde for all samples in the level or for each node (T)
bw	the smoothing bandwidth used: integer (if want to have it constant throughout) or vector of length L, where L is the number of levels in the hierarchy

Value

none

Examples

input_samples <- base_rejection_sampler_exp_4(beta = 1/4, nsamples = 100000,  proposal_mean = 0, proposal_sd = 1.5, dominating_M = 1.4)
test <- parallel_h_fusion_exp_4(N_schedule = rep(10000, 2), time_schedule = rep(1, 2),
                                start_beta = 1/4, C_schedule = rep(2, 2), L = 3,
                                base_samples = input_samples, study = T)

# plot result
plot_levels_hier_exp_4(test, from = -3, to = 3, plot_rows = 3, plot_columns = 1)

rchan26/exp4FusionRCPP documentation built on Nov. 6, 2019, 7:01 p.m.