R/diagis-package.R

In helske/diagis: Diagnostic Plot and Multivariate Summary Statistics of Weighted Samples from Importance Sampling

#' Auxiliary functions and diagnostic plots for importance sampling
#'
#' This package contains functions computing weighted (running) summaries and diagonostic plots 
#' for importance sampling problems. 
#' 
#' @docType package
#' @name diagis
#' @aliases diagis diagis-package
#' @importFrom coda mcmc
#' @importFrom stats ts
#' @importFrom Rcpp evalCpp
#' @useDynLib diagis
#' @examples
#' # simple importance sampling example
#' # true distribution is a standard normal:
#' p <- function(x) dnorm(x)
#' # proposal distribution is normal with sd s
#' q <- function(x, s) dnorm(x, 0, s)
#' 
#' # IS weights have finite variance only if s^2 > 1/2
#' # variance is s/(2-1/s^2)^(3/2)
#' 
#' #optimal case
#' set.seed(42)
#' s_opt <- sqrt(2)
#' x_opt <- rnorm(1000, sd = s_opt)
#' w_opt <- p(x_opt) / q(x_opt, s_opt) 
#' weighted_mean(x_opt, w_opt)
#' weighted_var(x_opt, w_opt)
#' s_inf <- 0.25
#' x_inf <- rnorm(1000, sd = s_inf)
#' w_inf <- p(x_inf) / q(x_inf, s_inf)
#' weighted_mean(x_inf, w_inf) #!!
#' weighted_var(x_inf, w_inf) #!!
#' # diagnostic plots
#' weight_plot(w_inf)
#' weight_plot(w_opt)
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