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PMCMRplus (version 1.9.3)

scheffeTest: Scheffe's Test

Description

Performs Scheffe's all-pairs comparisons test for normally distributed data with equal group variances.

Usage

scheffeTest(x, ...)

# S3 method for default scheffeTest(x, g, ...)

# S3 method for formula scheffeTest(formula, data, subset, na.action, ...)

# S3 method for aov scheffeTest(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").

Value

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 Scheffe's test can be performed. 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). Scheffe's all-pairs test statistics are given by

$$ t_{ij} \frac{\bar{X}_i - \bar{X_j}} {s_{\mathrm{in}} \left(1/n_j + 1/n_i\right)^{1/2}}, ~~ (i \ne j) $$

with \(s^2_{\mathrm{in}}\) the within-group ANOVA variance. The null hypothesis is rejected if \(t^2_{ij} > F_{v_{1}v_{2}\alpha}\), with \(v_1 = k - 1, ~ v_2 = N - k\) degree of freedom. The p-values are computed from the FDist distribution.

References

Bortz, J. (1993) Statistik f<U+00FC>r Sozialwissenschaftler. 4. Aufl., Berlin: Springer.

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

Scheffe, H. (1953) A Method for Judging all Contrasts in the Analysis of Variance, Biometrika 40, 87--110.

See Also

FDist, tukeyTest

Examples

Run this code
# NOT RUN {
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 <- scheffeTest(fit)
summary(res)
summaryGroup(res)
# }

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