Description Usage Arguments Value Author(s) References See Also Examples

Scheffe's method applies to the set of estimates of all possible contrasts among the factor level means, not just the pairwise differences considered by Tukey's method.

1 2 3 4 5 6 7 8 | ```
ScheffeTest(x, ...)
## S3 method for class 'aov'
ScheffeTest(x, which = NULL, contrasts = NULL,
conf.level = 0.95, ...)
## Default S3 method:
ScheffeTest(x, g = NULL, which = NULL,
contrasts = NULL, conf.level = 0.95, ...)
``` |

`x` |
either a fitted model object, usually an |

`g` |
the grouping variable. |

`which` |
character vector listing terms in the fitted model for which the intervals should be calculated. Defaults to all the terms. |

`contrasts` |
a |

`conf.level` |
numeric value between zero and one giving the confidence level to use. If this is set to NA, just a matrix with the p-values will be returned. |

`...` |
further arguments, currently not used. |

A list of classes `c("PostHocTest")`

, with one component for each term requested in `which`

. Each component is a matrix with columns `diff`

giving the difference in the observed means, `lwr.ci`

giving the lower end point of the interval, `upr.ci`

giving the upper end point and `pval`

giving the p-value after adjustment for the multiple comparisons.

There are print and plot methods for class `"PostHocTest"`

. The plot method does not accept `xlab`

, `ylab`

or `main`

arguments and creates its own values for each plot.

Andri Signorell <[email protected]>

Robert O. Kuehl, Steel R. (2000) *Design of experiments*. Duxbury

Steel R.G.D., Torrie J.H., Dickey, D.A. (1997) *Principles and Procedures of Statistics, A Biometrical Approach*. McGraw-Hill

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
fm1 <- aov(breaks ~ wool + tension, data = warpbreaks)
ScheffeTest(x=fm1)
ScheffeTest(x=fm1, which="tension")
TukeyHSD(fm1)
# some special contrasts
y <- c(7,33,26,27,21,6,14,19,6,11,11,18,14,18,19,14,9,12,6,
24,7,10,1,10,42,25,8,28,30,22,17,32,28,6,1,15,9,15,
2,37,13,18,23,1,3,4,6,2)
group <- factor(c(1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,3,
3,3,3,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6))
r.aov <- aov(y ~ group)
ScheffeTest(r.aov, contrasts=matrix( c(1,-0.5,-0.5,0,0,0,
0,0,0,1,-0.5,-0.5), ncol=2) )
# just p-values:
ScheffeTest(r.aov, conf.level=NA)
``` |

```
Posthoc multiple comparisons of means : Scheffe Test
95% family-wise confidence level
$wool
diff lwr.ci upr.ci pval
B-A -5.777778 -14.92513 3.369576 0.3526
$tension
diff lwr.ci upr.ci pval
M-L -10.000000 -21.20317 1.203174 0.0970 .
H-L -14.722222 -25.92540 -3.519048 0.0050 **
H-M -4.722222 -15.92540 6.480952 0.6869
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Posthoc multiple comparisons of means : Scheffe Test
95% family-wise confidence level
$tension
diff lwr.ci upr.ci pval
M-L -10.000000 -21.20317 1.203174 0.0970 .
H-L -14.722222 -25.92540 -3.519048 0.0050 **
H-M -4.722222 -15.92540 6.480952 0.6869
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = breaks ~ wool + tension, data = warpbreaks)
$wool
diff lwr upr p adj
B-A -5.777778 -12.12841 0.5728505 0.0736137
$tension
diff lwr upr p adj
M-L -10.000000 -19.35342 -0.6465793 0.0336262
H-L -14.722222 -24.07564 -5.3688015 0.0011218
H-M -4.722222 -14.07564 4.6311985 0.4474210
Posthoc multiple comparisons of means : Scheffe Test
95% family-wise confidence level
$group
diff lwr.ci upr.ci pval
1-2,3 7.2500 -6.417446 20.91745 0.6367
4-5,6 14.0625 0.395054 27.72995 0.0401 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Posthoc multiple comparisons of means : Scheffe Test
$group
1 2 3 4 5
2 0.927 - - - -
3 0.531 0.977 - - -
4 0.848 0.273 0.054 - -
5 0.940 1.000 0.970 0.296 -
6 0.400 0.934 1.000 0.031 0.920
```

DescTools documentation built on March 19, 2018, 9:03 a.m.

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