Monte.Carlo.se (version 0.1.1)

mc.se.vector: Standard Errors for Monte Carlo Output Summaries

Description

mc.se.vector --- gives jackknife and bootstrap SEs for a vector of MC output (say N estimates)

Usage

mc.se.vector(x, summary.f, B = 0, seed = NULL, ...)

Value

Returns data frame of summary.f , MC standard error of summary.f, MC sample size N, method (jackknife or bootstrap), B and seed if bootstrap is used

Arguments

x

vector from N independent Monte Carlo replications

summary.f

summary function computed from x (e.g., mean, median, var)

B

B=0 means use jackknife (default), B>0 means use bootstrap with B resamples, If B>0, then a seed must be given to start the bootstrap resampling

seed

seed=NULL (default) used with jackknife, otherwise needs a positive integer

...

Additional arguments to be passed

Author

Dennis Boos, Kevin Matthew, Jason Osborne

Details

Suppose that an N-vector of Monte Carlo output (thus, a sample of size N) is produced from N independent Monte Carlo samples, and a summary statistic like the mean or variance is to be reported in a table. mc.se.vector gives Monte Carlo standard errors (SEs) for these summary statistics. The vignette vignette("Example1", package = "Monte.Carlo.se") is a detailed account of using mc.se.vector.

References

Boos, D. D., and Osborne, J. A. (2015), "Assessing Variability of Complex Descriptive Statistics in Monte Carlo Studies using Resampling Methods," International Statistical Review, 25, 775-792.

Examples

# \donttest{
# First create MC output used for Table 9.1, p. 367, of Boos and Stefanski (2013)
# norm15 holds 10,000 sample means, 20% trimmed means, and medians
# computed from normal samples of size 15

N<-10000
set.seed(346)                   # sets the random number seed
z <- matrix(rnorm(N*15),nrow=N) # N rows of N(0,1) samples, n=15
out.m.15 <- apply(z,1,mean)     # mean for each sample
out.t20.15 <- apply(z,1,mean,trim=0.20)   # 20% trimmed mean for each sample
out.med.15 <- apply(z,1,median)           # median for each sample

# Save all 1000 blocks of 3 estimators in a data frame

norm15 <- data.frame(mean=out.m.15,trim20=out.t20.15,median=out.med.15)

# Compute the jackknife Standard Error (SE) for summary.f=mean
# This summary is useful for estimating the bias of estimators.

mc.se.vector(norm15[,1],summary.f=mean)
#        summary         se     N    method
# 1 -0.001920642 0.00256714 10000 Jackknife

# Compute a bootstrap SE for summary.f=mean

mc.se.vector(norm15[,1],B=1000,seed=4822,summary.f=mean)
#        summary          se     n    method    B seed
# 1 -0.001920642 0.002573516 10000 Bootstrap 1000 4822

# compare to basic R

mean(norm15[,1])
# [1] -0.001920642

sd(norm15[,1])/sqrt(10000)
# [1] 0.00256714

# Illustrate use with summaries having additional parameters.
# Compute the jackknife SE for summary.f=varn
# Multiplying the variance by sample size n allows comparison for different n

varn=function(x,n){n*var(x)}

# The additional parameter n replaces the ... in the mc.se.vector definition

mc.se.vector(norm15[,1],summary.f=varn,n=15)
#     summary         se     N    method
# 1 0.9885314 0.01407249 10000 Jackknife

# Compute a bootstrap SE for summary.f=varn

mc.se.vector(norm15[,1],B=1000,seed=3029,summary.f=varn,n=15)
#     summary         se     n    method    B seed
# 1 0.9885314 0.01408173 10000 Bootstrap 1000 3029
# }

# Function Code

mc.se.vector <- function(x,summary.f,B=0,seed=NULL,...){
# x is a vector from N independent Monte Carlo replications
# summary.f is a summary function computed from MC output
# ... is for additional arguments to summary.f
# B=0 means use jackkife, B>0 means use bootstrap with B resamples
 if (B>0){
  if(is.null(seed))stop('If B>0, then seed for the bootstrap must be given in the call')
  set.seed(seed);se=boot.se(x,B=B,theta = summary.f,...)}
 else{se = jack.se(x,theta = summary.f,...)}
summ = summary.f(x,...)
if(B>0){out=data.frame(summary=summ,se,n=length(x),method="Bootstrap",B,seed)}
 else{out=data.frame(summary=summ,se,N=length(x),method="Jackknife")}
return(out)}