knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

Many widely used and powerful statistical analysis commands --- such as `lm`

, `glm`

, `lme4::lmer`

, etc --- have a simple and consistent calling syntax, often involving a "formula" (e.g., `y ~ x`

), which makes them consistent, and easy to remember and apply.
Some other functions, even simple ones, don't use the formula syntax, or can be a bit awkward to use in some contexts, or require default values of arguments to be explicitly overridden.
In the `psyntur`

, there are some tools that aim to make this functions easier to apply.

These functions and the accompanying data sets can be loaded with the usual `library`

command.

```
library(psyntur)
```

`t_test`

R's `stats::t.test`

makes it easy to perform independent, paired, or one-sample t-tests.
For the independent sample t-test, the default is the Welch two sample t-test.
While arguably a good choice in practice, when t-tests are being taught to illustrate a simple example of normal linear model, the assumption of homogeneity of variance is used.
To use this with `t.test`

, this requires `var.equal = TRUE`

to be used.
The `t_test`

function is `psyntur`

is used when the standard independent t-test with homogeneity of variance is the desired default test.
For example, in the following, we use it with the `faithfulfaces`

data set.

t_test(trustworthy ~ face_sex, data = faithfulfaces)

`paired_t_test`

For paired t-tests, the `paired_t_test`

function can be used.
In this function, a formula is not used.
Instead, two variables in the same data frame, which are assumed to be paired in some manner, are used.
For example, the `pairedsleep`

data set (included in `psyntur`

) is as follows.

pairedsleep

This gives the difference from control in number of hours slept by `r nrow(pairedsleep)`

different patients when each took two different drugs.
These time differences under the two drugs are `y1`

and `y2`

.
A paired samples t-test can be performed as follows with this data.

paired_t_test(y1, y2, data = pairedsleep)

`pairwise_t_test`

For independent t-tests applied all pairs of a set of variables, to which p-value adjustments are applied, we can use `pairwise_t_test`

.
For example, the following creates a categorical variable with four values, which are the interaction of two binary variables.

data_df <- dplyr::mutate(vizverb, IV = interaction(task, response))

Independent samples t-tests with Bonferroni corrections on the `time`

variable applied to all pairs of the four levels of the `IV`

variable can be done as follows.

pairwise_t_test(time ~ IV, data = data_df)

`shapiro_test`

The Shapiro-Wilk test of normality can be applied to a single numeric vector in a data frame as in the following example.

shapiro_test(time, data = data_df)

To test the normality of each subset of a variable, such as `time`

, corresponding to the values of a categorical variable, we can use a `by`

variable as in the following example.

shapiro_test(time, by = IV, data = data_df)

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