# pairedSamplesTTest

##### Paired samples t-test

Convenience function that runs a paired samples t-test. This is a wrapper function intended to be used for pedagogical purposes only.

##### Usage

```
pairedSamplesTTest(
formula, data=NULL, id=NULL,
one.sided=FALSE, conf.level=.95
)
```

##### Arguments

- formula
- Formula specifying the outcome and the groups (required).
- data
- Optional data frame containing the variables.
- id
- The name of the id variable (must be a character string).
- one.sided
- One sided or two sided hypothesis test (default =
`FALSE`

) - conf.level
- The confidence level for the confidence interval (default = .95).

##### Details

The `pairedSamplesTTest`

function runs a paired-sample t-test, and prints the results in a format that is easier for novices to handle than the output of `t.test`

. All the actual calculations are done by the `t.test`

and `cohensD`

functions.

There are two different ways of specifying the formula, depending on whether the data are in wide form or long form. If the data are in wide form, then the input should be a one-sided formula of the form `~ variable1 + variable2`

. The `id`

variable is not required: the first element of `variable1`

is paired with the first element of `variable2`

and so on. Both `variable1`

and `variable2`

must be numeric.

If the data are in long form, a two sided formula is required. The simplest way to specify the test is to input a formula of the form `outcome ~ group + (id)`

. The term in parentheses is assumed to be the `id`

variable, and must be a factor. The `group`

variable must be a factor with two levels (if there are more than two levels but only two are used in the data, a warning is given). The `outcome`

variable must be numeric.

The reason for using the `outcome ~ group + (id)`

format is that it is broadly consistent with the way repeated measures analyses are specified in the `lme4`

package. However, this format may not appeal to some people for teaching purposes. Given this, the `pairedSamplesTTest`

also supports a simpler formula of the form `outcome ~ group`

, so long as the user specifies the `id`

argument: this must be a character vector specifying the name of the id variable

As with the `t.test`

function, the default test is two sided, corresponding to a default value of `one.sided = FALSE`

. To specify a one sided test, the `one.sided`

argument must specify the name of the factor level (long form data) or variable (wide form data) that is hypothesised (under the alternative) to have the larger mean. For instance, if the outcome at "time2" is expected to be higher than at "time1", then the corresponding one sided test is specified by `one.sided = "time2"`

.

##### Value

##### Note

`pairedSamplesTTest`

also supports an even more "lme4"-like method for specifying the model in the `formula`

argument. That is, `outcome ~ group + (1|id)`

is deemed to be equivalent to `outcome ~ group + (id)`

. This may be removed in future versions.

##### Warning

This package is under development, and has been released only due to teaching constraints. Until this notice disappears from the help files, you should assume that everything in the package is subject to change. Backwards compatibility is NOT guaranteed. Functions may be deleted in future versions and new syntax may be inconsistent with earlier versions. For the moment at least, this package should be treated with extreme caution.

##### See Also

##### Examples

`library(lsr)`

```
# long form data frame
df <- data.frame(
id = factor( x=c(1, 1, 2, 2, 3, 3, 4, 4),
labels=c("alice","bob","chris","diana") ),
time = factor( x=c(1,2,1,2,1,2,1,2),
labels=c("time1","time2")),
wm = c(3, 4, 6, 6, 9, 12,7,9)
)
# wide form
df2 <- longToWide( df, wm ~ time )
# basic test, run from long form or wide form data
pairedSamplesTTest( formula= wm ~ time, data=df, id="id" )
pairedSamplesTTest( formula= wm ~ time + (id), data=df )
pairedSamplesTTest( formula= ~wm_time1 + wm_time2, data=df2 )
# one sided test
pairedSamplesTTest( formula= wm~time, data=df, id="id", one.sided="time2" )
# missing data because of NA values
df$wm[1] <- NA
pairedSamplesTTest( formula= wm~time, data=df, id="id" )
# missing data because of missing cases from the long form data frame
df <- df[-1,]
pairedSamplesTTest( formula= wm~time, data=df, id="id" )
```

*Documentation reproduced from package lsr, version 0.5, License: GPL-3*