| Type: | Package |
| Title: | Diagnostic Plot and Multivariate Summary Statistics of Weighted Samples from Importance Sampling |
| Version: | 0.2.3 |
| Description: | Fast functions for effective sample size, weighted multivariate mean, variance, and quantile computation, and weight diagnostic plot for generic importance sampling type or other probability weighted samples. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| BugReports: | https://github.com/helske/diagis/issues |
| URL: | https://github.com/helske/diagis/ |
| Suggests: | covr, knitr, rmarkdown, testthat, |
| Imports: | coda, ggplot2 (≥ 2.1.0), gridExtra, Rcpp (≥ 0.12.7) |
| LinkingTo: | Rcpp, RcppArmadillo |
| RoxygenNote: | 7.2.3 |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| NeedsCompilation: | yes |
| Packaged: | 2023-09-04 10:07:02 UTC; jvhels |
| Author: | Jouni Helske 
[aut, cre] |
| Maintainer: | Jouni Helske <jouni.helske@iki.fi> |
| Repository: | CRAN |
| Date/Publication: | 2023-09-05 08:50:02 UTC |
Auxiliary functions and diagnostic plots for importance sampling
Description
This package contains functions computing weighted (running) summaries and diagonostic plots for importance sampling problems.
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)
Effective sample size
Description
Computes the effective sample size (ESS) of importance sampling estimator.
Usage
ess(w, f, x)
Arguments
w |
A numeric vector of non-negative weights. |
f |
A function used in computing |
x |
A numeric vector of samples used to generate |
Value
An effective sample size estimate.
Running effective sample size
Description
Computes and returns the running estimate of effective sample size (ESS) of importance sampling estimator.
Usage
running_ess(w, f, x)
Arguments
w |
A numeric vector of non-negative weights. |
f |
A function used in computing |
x |
A numeric vector of samples used to generate |
Value
An effective sample size estimate.
Running mean
Description
Computes running mean of a vector or matrix, returning the values from each step.
Usage
running_mean(x, na.rm)
Arguments
x |
A numeric vector, matrix, three dimensional array, or an |
na.rm |
If |
Value
A vector containing the recursive mean estimates.
Running variance of a vector
Description
Computes running variance of a vector, returning the values from each step.
Usage
running_var(x, method = c("moment", "unbiased"), na.rm = FALSE)
Arguments
x |
A numeric vector or object that can be coerced to such. |
method |
Estimator type, either |
na.rm |
If |
Value
A vector containing the recursive variance estimates.
Running weighted mean
Description
Computes running weighted mean of a vector or matrix, returning the values from each step.
Usage
running_weighted_mean(x, w, na.rm)
Arguments
x |
A numeric vector, matrix, three dimensional array, or an |
w |
A numeric vector of non-negative weights. Will be automatically normalised to sum to one. |
na.rm |
If |
Value
A vector containing the recursive weighted mean estimates.
Running weighted variance of a vector
Description
Computes running weighted variance of a vector, returning the values from each step.
Usage
running_weighted_var(x, w, method = c("moment", "unbiased"), na.rm = FALSE)
Arguments
x |
A numeric vector or object that can be coerced to such. |
w |
A numeric vector of non-negative weights. Will be automatically normalised to sum to one. |
method |
Estimator type, either |
na.rm |
If |
Value
A vector containing the recursive weighted variance estimates.
Diagnostic plot of importance sampling weights
Description
Function weight_plot plots four figures given the weight vector w:
Plot of largest weights, sorted graph of all weights, running variance estimate of weights,
and running effective sample size estimate of weights.
Usage
weight_plot(w)
Arguments
w |
Vector of weights. |
Examples
#' importance sampling from too narrow distribution
#' weights have infinite variance
set.seed(1)
x_inf <- rnorm(1000, sd = 0.1)
w_inf <- dnorm(x_inf) / dnorm(x_inf, 0, 0.1)
weight_plot(w_inf)
x_opt <- rnorm(1000, sd = sqrt(2))
w_opt <- dnorm(x_opt) / dnorm(x_opt, 0, sqrt(2))
weight_plot(w_opt)
Weighted mean
Description
Computes a weighted mean of a vector, matrix, or a three dimensional array.
Usage
weighted_mean(x, w, na.rm)
Arguments
x |
A numeric vector, matrix, three dimensional array, or an |
w |
A numeric vector of non-negative weights. Will be automatically normalised to sum to one. |
na.rm |
If |
Value
A weighted mean.
Weighted quantiles
Description
Computes a weighted quantiles of a vector or matrix. Based on the formula in Wikipedia (see the vignette) which is one of many ways to compute weighted quantiles.
Usage
weighted_quantile(x, w, probs = probs, na.rm)
Arguments
x |
A numeric vector or matrix. For matrix, the quantiles are computed for each column. |
w |
A numeric vector of non-negative weights. Will be automatically normalised to sum to one. |
probs |
A numeric vector of probabilities with values between 0 and 1. |
na.rm |
If |
Value
A weighted variance.
Note
Compared to some other R functions, here the weights are regarded as probability weights, not frequency weights.
Weighted standard error
Description
Computes a weighted standard error of a vector or matrix.
Usage
weighted_se(x, w, na.rm)
Arguments
x |
A numeric vector or matrix. For matrix, standard errors are computed for each column |
w |
A numeric vector of non-negative weights. Will be automatically normalised to sum to one. |
na.rm |
If |
Value
A weighted variance.
Note
Compared to some other R functions, here the weights are regarded as probability weights, not frequency weights.
Weighted covariance
Description
Computes a weighted variance/covariance of a vector, matrix or a three dimensional array.
Usage
weighted_var(x, w, method, na.rm)
Arguments
x |
A numeric vector, matrix or three dimensional array. For matrix, covariances are computed between columns. For array, marginal covariances are computed for each column, i.e. for $m x n x k$ array function returns $m x m x n$ array. |
w |
A numeric vector of non-negative weights. Will be automatically normalised to sum to one. |
method |
Estimator type, either |
na.rm |
If |
Value
A weighted variance.
Note
Compared to some other R functions, here the weights are regarded as probability weights, not frequency weights.