# yuend: Paired samples robust t-tests. In WRS2: A Collection of Robust Statistical Methods

 yuend R Documentation

## Paired samples robust t-tests.

### Description

The function `yuend` performs Yuen's test on trimmed means for dependent samples. `Dqcomhd` compares the quantiles of the marginal distributions associated with two dependent groups via hd estimator. Tied values are allowed. `dep.effect` computes various effect sizes and confidence intervals for two dependent samples (see Details).

### Usage

```yuend(x, y, tr = 0.2, ...)
Dqcomhd(x, y, q = c(1:9)/10, nboot = 1000, na.rm = TRUE, ...)
dep.effect(x, y, tr = 0.2, nboot = 1000, ...)
```

### Arguments

 `x` an numeric vector of data values (e.g. for time 1). `y` an numeric vector of data values (e.g. for time 2). `tr` trim level for the means. `q` quantiles to be compared. `nboot` number of bootstrap samples. `na.rm` whether missing values should be removed. `...` currently ignored.

### Details

The test statistic is a paired samples generalization of Yuen's independent samples t-test on trimmed means.

`dep.effect` computes the following effect sizes:

AKP: robust standardized difference similar to Cohen's d

QS: Quantile shift based on the median of the distribution of difference scores,

QStr: Quantile shift based on the trimmed mean of the distribution of X-Y

SIGN: P(X<Y), probability that for a random pair, the first is less than the second.

### Value

`yuend` returns an object of class `"yuen"` containing:

 `test` value of the test statistic (t-statistic) `p.value` p-value `conf.int` confidence interval `df` degress of freedom `diff` trimmed mean difference `call` function call

`Dqcomhd` returns an object of class `"robtab"` containing:

 `partable` parameter table

`dep.effect` returns a matrix with the null value of the effect size, the estimated effect size, small/medium/large conventions, and lower/upper CI bounds.

### References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

`yuen`, `qcomhd`

### Examples

```## Cholesterol data from Wilcox (2012, p. 197)
before <- c(190, 210, 300,240, 280, 170, 280, 250, 240, 220)
after <- c(210, 210, 340, 190, 260, 180, 200, 220, 230, 200)
yuend(before, after)

set.seed(123)
Dqcomhd(before, after, nboot = 200, q = c(0.25, 0.5, 0.75))

set.seed(123)
dep.effect(before, after)
```

WRS2 documentation built on June 10, 2022, 5:09 p.m.