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```
## Daniel Gerlanc and Kris Kirby (2010-2012)
## Functions for calculating unstandardized means and differences in
## unstandardized means. 'calcUnstandardizedMean' is the most
## flexible, allowing the means to be weighted and one or more
## distinct samples. 'meanDiffBoot' and 'meanUnweightedBoot' are more
## specialized, and duplicate some of the functionality of
## 'calcUnstandardizedMean'.
calcUnstandardizedMean <- function(vals, freq, grps, lambdas) {
## Compute an unstandardized mean of one or more groups
##
## Args:
## vals: a numeric vector of values
## freq: a frequency vector of length equal to the number of records in
## 'data' indicating how many times an observation should be drawn
## from each sample.
## grps: an optional grouping vector of the same length as 'vals'
## lambdas: an optional named vector of weights to scale the means
##
## Details:
## This function is meant to be passed as the 'statistic' argument
## to the 'boot' function. 'freq' should be a frequency vector of
## the type returned by 'boot' If no 'grps' argument is passed, then
## all values are treated as if they are in the same group. If no 'lambdas'
## argument is passed, then each group is assigned the same weight
##
## Note:
## To calculate a difference in means, set the lambdas to +1, -1
##
## Returns:
## The bootstrap, unstandardized mean
## Handle a missing 'grps' argument by assigning all values to the same group
if (missing(grps))
grps = rep.int(1, length(vals))
## Get the integer indices of the different groups.
grp.idx = split(seq_along(vals), grps, drop=TRUE)
grp.nms = names(grp.idx)
n.grps = length(grp.nms)
## Default behavior for missing lambdas is to give each group an equal weight
if (missing(lambdas))
lambdas = structure(rep.int(1 / n.grps, n.grps), names=grp.nms)
## Calculate the mean for each group
means = numeric()
for (nm in grp.nms) {
this.grp.idx = grp.idx[[nm]]
sample.vals = rep(vals[this.grp.idx], times=freq[this.grp.idx])
means[nm] = mean(sample.vals)
}
## Put means and lambdas in the same order
lambdas = lambdas[names(means)]
res = sum(lambdas * means)
}
meanUnweightedBoot <- function(vals, freq, grps) {
## Computes the mean of the means of multiple samples
##
## Args:
## vals: a numeric vector of values
## freq: a frequency vector of the same length as 'vals'
## indicating how many times an observation should be drawn
## from each sample.
## grps: a required grouping vector of the same length as 'vals'
##
## Details:
## This function is meant to be passed as the 'statistic' argument
## to the 'boot' function. 'freq' should be a frequency vector of
## the type returned by 'boot'
##
## Returns:
## The mean of the bootstrap means of multiple samples
## Error Handling
if (missing(grps))
stop("'grps' argument cannot be missing.")
## Get the indices of the values corresponding to the different samples.
idx = split(seq_along(vals), grps, drop=TRUE)
n = length(idx)
means = numeric()
for (i in idx) {
sample.vals = rep(vals[i], times=freq[i])
means = append(means, mean(sample.vals))
}
res = mean(means)
res
}
meanDiffBoot <- function(vals, freq, grps) {
## Computes the difference in means between 2 samples
##
## Args:
## vals: a numeric vector of values
## freq: a frequency vector of the same length as 'vals'
## indicating how many times an observation should be drawn
## from each sample.
## grps: a required grouping vector of the same length as 'vals'
##
## Details:
## This function is meant to be passed as the 'statistic' argument
## to the 'boot' function. 'freq' should be a frequency vector of
## the type returned by 'boot'
##
## Returns:
## The difference in means between 2 samples
## Error Handling
if (missing(grps))
stop("'grps' argument cannot be missing.")
## Get the indices of the values corresponding to the different samples.
idx = split(seq_along(vals), grps, drop=TRUE)
n = length(idx)
if (n != 2)
stop("There must be 2 groups for an estimation of differences in means. Only ", n,
" groups were found.")
means = numeric()
for (i in idx) {
sample.vals = rep(vals[i], times=freq[i])
means = append(means, mean(sample.vals))
}
res <- means[1] - means[2]
res
}
```

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