MeanDiffCI: Confidence Intervals for Difference of Means

Description

Calculates the confidence interval for the difference of two means either the classical way or with the bootstrap approach.

Usage

MeanDiffCI(x, ...)

## S3 method for class 'default':
MeanDiffCI(x, y, method = c("classic", "norm", "basic", "stud", "perc", "bca"), 
           conf.level = 0.95, na.rm = FALSE, R = 999, ...)
           
## S3 method for class 'formula':
MeanDiffCI(formula, data, subset, na.action, ...)

Arguments

a (non-empty) numeric vector of data values.

method

a vector of character strings representing the type of intervals required. The value should be any subset of the values "classic", "norm", "basic", "stud", "perc", "bca".

conf.level

confidence level of the interval.

na.rm

logical. Should missing values be removed? Defaults to FALSE.

the number of bootstrap replicates. Usually this will be a single positive integer. For importance resampling, some resamples may use one set of weights and others use a different set of weights. In this case R would be a vector of integers where each com

formula

a formula of the form lhs ~ rhs where lhs is a numeric variable giving the data values and rhs a factor with two levels giving the corresponding groups.

data

an optional matrix or data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula)

subset

an optional vector specifying a subset of observations to be used.

na.action

a function which indicates what should happen when the data contain NAs. Defaults to getOption("na.action").

...

further argument to be passed to or from methods.

Value

a numeric vector with 3 elements:
meandiffthe difference: mean(x) - mean(y)
lwr.cilower bound of the confidence interval
upr.ciupper bound of the confidence interval

Details

This function collects code from two sources. The classical confidence interval is calculated by means of t.test. The bootstrap intervals are strongly based on the example in boot.

Examples

Run this code

x <- d.pizza$price[d.pizza$driver=="Carter"]
y <- d.pizza$price[d.pizza$driver=="Miller"]

MeanDiffCI(x, y, na.rm=TRUE)
MeanDiffCI(x, y, conf.level=0.99, na.rm=TRUE)

# the different types of bootstrap confints
MeanDiffCI(x, y, method="norm", na.rm=TRUE)
MeanDiffCI(x, y, method="basic", na.rm=TRUE)
# MeanDiffCI(x, y, method="stud", na.rm=TRUE)
MeanDiffCI(x, y, method="perc", na.rm=TRUE)
MeanDiffCI(x, y, method="bca", na.rm=TRUE)

# the formula interface
MeanDiffCI(price ~ driver, data=d.pizza, subset=driver %in% c("Carter","Miller"))

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