`MeanDiffCI.Rd`

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

```
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, ...)
```

- 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.

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`

.

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

```
x <- d.pizza$price[d.pizza$driver=="Carter"]
y <- d.pizza$price[d.pizza$driver=="Miller"]
MeanDiffCI(x, y, na.rm=TRUE)
#> meandiff lwr.ci upr.ci
#> -4.5735546 -9.2621803 0.1150712
MeanDiffCI(x, y, conf.level=0.99, na.rm=TRUE)
#> meandiff lwr.ci upr.ci
#> -4.573555 -10.752249 1.605140
# the different types of bootstrap confints
MeanDiffCI(x, y, method="norm", na.rm=TRUE)
#> meandiff lwr.ci upr.ci
#> -4.5735546 -9.0779471 -0.1159996
MeanDiffCI(x, y, method="basic", na.rm=TRUE)
#> meandiff lwr.ci upr.ci
#> -4.573555 -9.105721 0.109601
# MeanDiffCI(x, y, method="stud", na.rm=TRUE)
MeanDiffCI(x, y, method="perc", na.rm=TRUE)
#> meandiff lwr.ci upr.ci
#> -4.573555 -9.302855 0.129432
MeanDiffCI(x, y, method="bca", na.rm=TRUE)
#> meandiff lwr.ci upr.ci
#> -4.5735546 -9.1973690 -0.1745148
# the formula interface
MeanDiffCI(price ~ driver, data=d.pizza, subset=driver %in% c("Carter","Miller"))
#> meandiff lwr.ci upr.ci
#> -4.5735546 -9.2621803 0.1150712
```