yuend function

Paired samples robust t-tests.

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).

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.

Returns

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.

See Also

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)