lsdTest function

Least Significant Difference Test

Performs the least significant difference all-pairs comparisons test for normally distributed data with equal group variances.

lsdTest(x, ...)

## Default S3 method:
lsdTest(x, g, ...)

## S3 method for class 'formula'
lsdTest(formula, data, subset, na.action, ...)

## S3 method for class 'aov'
lsdTest(x, ...)

Arguments

• x: a numeric vector of data values, a list of numeric data vectors or a fitted model object, usually an aov fit.
• ...: further arguments to be passed to or from methods.
• g: a vector or factor object giving the group for the corresponding elements of "x". Ignored with a warning if "x" is a list.
• formula: a formula of the form response ~ group where response gives the data values and group a vector or factor of 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").

Returns

A list with class "PMCMR" containing the following components:

• method: a character string indicating what type of test was performed.
• data.name: a character string giving the name(s) of the data.
• statistic: lower-triangle matrix of the estimated quantiles of the pairwise test statistics.
• p.value: lower-triangle matrix of the p-values for the pairwise tests.
• alternative: a character string describing the alternative hypothesis.
• p.adjust.method: a character string describing the method for p-value adjustment.
• model: a data frame of the input data.
• dist: a string that denotes the test distribution.

Details

For all-pairs comparisons in an one-factorial layout with normally distributed residuals and equal variances the least signifiant difference test can be performed after a significant ANOVA F-test. Let $X_{ij}$ denote a continuous random variable with the $j$-the realization ($1 \le j \le n_i$) in the $i$-th group ($1 \le i \le k$). Furthermore, the total sample size is $N = \sum_{i=1}^k n_i$. A total of $m = k(k-1)/2$

hypotheses can be tested: The null hypothesis is H$_{ij}: \mu_i = \mu_j ~~ (i \ne j)$ is tested against the alternative A$_{ij}: \mu_i \ne \mu_j$ (two-tailed). Fisher's LSD all-pairs test statistics are given by

with $s^2_{\mathrm{in}}$ the within-group ANOVA variance. The null hypothesis is rejected if $|t_{ij}| > t_{v\alpha/2}$, with $v = N - k$ degree of freedom. The p-values (two-tailed) are computed from the TDist distribution.

Note

As there is no p-value adjustment included, this function is equivalent to Fisher's protected LSD test, provided that the LSD test is only applied after a significant one-way ANOVA F-test. If one is interested in other types of LSD test (i.e. with p-value adustment) see function pairwise.t.test.

Examples

fit <- aov(weight ~ feed, chickwts)
shapiro.test(residuals(fit))
bartlett.test(weight ~ feed, chickwts)
anova(fit)

## also works with fitted objects of class aov
res <- lsdTest(fit)
summary(res)
summaryGroup(res)

References

Sachs, L. (1997) Angewandte Statistik, New York: Springer.

See Also

TDist, pairwise.t.test