Package diagis. January 25, 2018
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1 Type Package Package diagis January 25, 2018 Title Diagnostic Plot and Multivariate Summary Statistics of Weighted Samples from Importance Sampling Version Date Author Jouni Helske Maintainer Jouni Helske Fast functions for effective sample size, eighted multivariate mean and variance computation, and eight diagnostic plot for generic importance sampling type results. License GPL (>= 2) BugReports Suggests testthat, knitr, rmarkdon Imports Rcpp (>= ), ggplot2 (>= 2.1.0), gridetra, coda LinkingTo Rcpp, RcppArmadillo RoygenNote VignetteBuilder knitr NeedsCompilation yes Repository CRAN Date/Publication :37:41 UTC R topics documented: diagis ess running_ess running_mean running_var running_eighted_mean running_eighted_var
2 2 diagis eighted_mean eighted_se eighted_var eight_plot Inde 9 diagis Auiliary functions and diagnostic plots for importance sampling This package contains functions computing eighted (running) summaries and diagonostic plots for importance sampling problems. Eamples # simple importance sampling eample # true distribution is a standard normal: p <- function() dnorm() # proposal distribution is normal ith sd s q <- function(, s) dnorm(, 0, s) # IS eights 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) _opt <- rnorm(1000, sd = s_opt) _opt <- p(_opt) / q(_opt, s_opt) eighted_mean(_opt, _opt) eighted_var(_opt, _opt) s_inf < _inf <- rnorm(1000, sd = s_inf) _inf <- p(_inf) / q(_inf, s_inf) eighted_mean(_inf, _inf) #!! eighted_var(_inf, _inf) #!! # diagnostic plots eight_plot(_inf) eight_plot(_opt)
3 ess 3 ess Effective sample size Computes the effective sample size (ESS) of importance sampling estimator. ess(, f, ) f A numeric vector of non-negative eights. A function used in computing f-specific ESS. A numeric vector of samples used to generate. Used for computing f(). An effective sample size estimate. running_ess Running effective sample size Computes and returns the running estimate of effective sample size (ESS) of importance sampling estimator. running_ess(, f, ) f A numeric vector of non-negative eights. A function used in computing f-specific ESS. A numeric vector of samples used to generate. Used for computing f(). An effective sample size estimate.
4 4 running_var running_mean Running mean Computes running mean of a vector or matri, returning the values from each step. running_mean(, ) A numeric vector, matri, three dimensional array, or an mcmc object from the coda package. For matri, the mean is computed for each column, and for array the seep is done over the third dimension. If TRUE, NA values in are omitted from the computation. Default is FALSE. A vector containing the recursive mean estimates. running_var Running variance of a vector Computes running variance of a vector, returning the values from each step. running_var(, method = c("moment", "unbiased"), = FALSE) method A numeric vector or object that can be coerced to such. Estimator type, either "moment" (default) or "unbiased", hich is unbiased only in case of frequency eights. If TRUE, NA values in are omitted from the computation. Default is FALSE. A vector containing the recursive variance estimates.
5 running_eighted_mean 5 running_eighted_mean Running eighted mean Computes running eighted mean of a vector or matri, returning the values from each step. running_eighted_mean(,, ) A numeric vector, matri, three dimensional array, or an mcmc object from the coda package. For matri, the mean is computed for each column, and for array the seep is done over the third dimension. A numeric vector of non-negative eights. Will be automatically normalised to If TRUE, NA values in (and corresponding eights in ) are omitted from the computation. Default is FALSE. Only used in vector methods. A vector containing the recursive eighted mean estimates. running_eighted_var Running eighted variance of a vector Computes running eighted variance of a vector, returning the values from each step. running_eighted_var(,, method = c("moment", "unbiased"), = FALSE) method A numeric vector or object that can be coerced to such. A numeric vector of non-negative eights. Will be automatically normalised to Estimator type, either "moment" (default) or "unbiased", hich is unbiased only in case of frequency eights. If TRUE, NA values in (and corresponding eights in ) are omitted from the computation. Default is FALSE.
6 6 eighted_se A vector containing the recursive eighted variance estimates. eighted_mean Weighted mean Computes a eighted mean of a vector, matri, or a three dimensional array. eighted_mean(,, ) A numeric vector, matri, three dimensional array, or an mcmc object from the coda package. For matri, the mean is computed for each column, and for array the seep is done over the third dimension. A numeric vector of non-negative eights. Will be automatically normalised to If TRUE, NA values in (and corresponding eights in ) are omitted from the computation. Default is FALSE. Only used in vector methods. A eighted mean. eighted_se Weighted standard error Computes a eighted standard error of a vector or matri. eighted_se(,, ) A numeric vector or matri. A numeric vector of non-negative eights. Will be automatically normalised to If TRUE, NA values in (and corresponding eights in ) are omitted from the computation. Default is FALSE.
7 eighted_var 7 A eighted variance. Note Compared to some other R functions, here the eights are regarded as probability eights, not frequency eights. eighted_var Weighted covariance Computes a eighted variance/covariance of a vector, matri or a three dimensional array. eighted_var(,, method, ) method A numeric vector, matri or three dimensional array. For matri, covariances are computed beteen columns. For array, marginal covariances are computed for each column, i.e. for $m n k$ array function returns $m m n$ array. A numeric vector of non-negative eights. Will be automatically normalised to Estimator type, either "moment" (default) or "unbiased", hich is unbiased only in case of frequency eights. If TRUE, NA values in (and corresponding eights in ) are omitted from the computation. Default is FALSE. A eighted variance. Note Compared to some other R functions, here the eights are regarded as probability eights, not frequency eights.
8 8 eight_plot eight_plot Diagnostic plot of importance sampling eights Function eight_plot plots four figures given the eight vector : Plot of largest eights, sorted graph of all eights, running variance estimate of eights, and running effective sample size estimate of eights. eight_plot() Vector of eights Eamples #' importance sampling from too narro distribution #' eights have infinite variance set.seed(1) _inf <- rnorm(1000, sd = 0.1) _inf <- dnorm(_inf) / dnorm(_inf, 0, 0.1) eight_plot(_inf) _opt <- rnorm(1000, sd = sqrt(2)) _opt <- dnorm(_opt) / dnorm(_opt, 0, sqrt(2)) eight_plot(_opt)
9 Inde diagis, 2 diagis-package (diagis), 2 ess, 3 running_ess, 3 running_mean, 4 running_var, 4 running_eighted_mean, 5 running_eighted_var, 5 eight_plot, 8 eighted_mean, 6 eighted_se, 6 eighted_var, 7 9
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