R: Confidence Interval For Difference of Means

MeanDiffCI {DescTools} | R Documentation

## Confidence Interval 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, ...)

    ## Default S3 method:
    MeanDiffCI(x, y, method = c("classic", "norm", "basic", "stud", "perc", "bca"),
               conf.level = 0.95, sides = c("two.sided", "left", "right"), paired = FALSE,
               na.rm = FALSE, R = 999, ...)

    ## S3 method for class 'formula'
    MeanDiffCI(formula, data, subset, na.action, ...)

### Arguments

`x` | a (non-empty) numeric vector of data values.
`y` | 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"`. See `boot.ci`.
`conf.level` | confidence level of the interval.
`sides` | a character string specifying the side of the confidence interval, must be one of `"two.sided"` (default), `"left"` or `"right"`. You can specify just the initial letter. `"left"` would be analogue to a hypothesis of `"greater"` in a `t.test`.
`paired` | a logical indicating whether you want confidence intervals for a paired design. Defaults to `FALSE`.
`na.rm` | logical. Should missing values be removed? Defaults to `FALSE`.
`R` | 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 component gives the number of resamples from each of the rows of weights. See `boot`.
`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.

### 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`.

### Value

a numeric vector with 3 elements:

`meandiff` | the difference: mean(x) - mean(y)
`lwr.ci` | lower bound of the confidence interval
`upr.ci` | upper bound of the confidence interval

### Author(s)

Andri Signorell <andri@signorell.net>

### See Also

`MeanCI`, `VarCI`, `MedianCI`, `boot.ci`

### Examples

    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"))

[Package DescTools version 0.99.28 Index]