Extra {Monte.Carlo.se}

Auxiliary Functions

Description

Auxiliary functions to be used in the Monte.Carlo.se package, mainly with mc.se.matrix. Scroll down to the Examples Section to see the actual code.

Usage

ratio.var(index, xdata)

ratio.sd(index, xdata)

ratio.mse(index, xdata, true)

ratio.mean.vhat.var(index, xdata)

ratio.mean.sdhat.sd(index, xdata)

corr(index, xdata)

cv(x)

varn(x, n)

jack.var(x, theta, ...)

boot.var(x, B, theta, ...)

Arguments

index

index = usually of the form 1:N

xdata

actual data

true

true parameter value when computing mean squared error

x

Input vector in calls to jack.var and boot.var

n

sample size

theta

theta = function in calls to jack.var and boot.var

...

Additional arguments to be passed

B

Bootstrap reps in calls to boot.var

Author(s)

Dennis Boos, Kevin Matthew, Jason Osborne

Examples

# These are extra functions included in the MCse package
# The following functions are to be used with mc.se.matrix

ratio.var <- function(index,xdata)         # ratio of variances
 {var(xdata[index,1])/var(xdata[index,2])}

# The above function is for the ratio of the sample variance of column 1 to
# the sample variance  of column 2 of xdata.
# Note that the actual data goes into xdata, the second argument of ratio.var.
# Example call for 10,000 means and medians:
# ratio.var(1:10000,xdata=cbind(out.m.15,out.med.15))

ratio.sd<-function(index,xdata){           # ratio of standard deviations
 sd(xdata[index,1])/sd(xdata[index,2])}

ratio.mse<-function(index,xdata,true){     # ratio of mean squared errors
 mean((xdata[index,1]-true)^2)/mean((xdata[index,2]-true)^2)}

ratio.mean.vhat.var<-function(index,xdata){# estimates in col 1, vhats in col. 2
 mean(xdata[index,2])/var(xdata[index,1])}

ratio.mean.sdhat.sd<-function(index,xdata){# estimates in col 1, SEs in col. 2
 mean(xdata[index,2])/sd(xdata[index,1])}

corr<-function(index,xdata){               # simple correlation
 cor(xdata[index,1],xdata[index,2])}

# These next two functions correspond to jack.se and boot.se.
# x is a data vector, and theta is a function applied to x.
# Each returns a variance estimate for theta(x).

jack.var <- function(x, theta, ...){  # jackknife estimate of variance
 n <- length(x)
 u <- rep(0, n)
 for(i in 1:n){u[i] <- theta(x[ - i], ...)}
 jack.var <-((n-1)/n)* sum((u-mean(u))^2)
 return(jack.var)}

boot.var <- function(x,B,theta, ...){ # bootstrap estimate of variance
 n <- length(x)
 bootsam <- matrix(sample(x,size = n*B,replace=T), nrow=B)
 thetastar <- apply(bootsam,1,theta,...)
 boot.var <- var(thetastar)
 return(boot.var)}