Scheffe 1959, method is very general in that all possible contrasts can be tested for significance and confidence intervals can be constructed for the corresponding linear. The test is conservative.
scheffe.test( y, trt, DFerror, MSerror, Fc, alpha = 0.05, group = TRUE, main = NULL, console = FALSE )
| y | model(aov or lm) or answer of the experimental unit |
|---|---|
| trt | Constant( only y=model) or vector treatment applied to each experimental unit |
| DFerror | Degrees of freedom |
| MSerror | Mean Square Error |
| Fc | F Value |
| alpha | Significant level |
| group | TRUE or FALSE |
| main | Title |
| console | logical, print output |
Statistics of the model
Design parameters
Statistical summary of the study variable
Comparison between treatments
Formation of treatment groups
It is necessary first makes a analysis of variance.
if y = model, then to apply the instruction:
scheffe.test (model, "trt",
alpha = 0.05, group = TRUE, main = NULL, console = FALSE)
where the model
class is aov or lm.
Robert O. Kuehl. 2nd ed. Design of experiments. Duxbury,
copyright 2000.
Steel, R.; Torri,J; Dickey, D.(1997) Principles and
Procedures of Statistics A Biometrical Approach. pp189
BIB.test, DAU.test,
duncan.test, durbin.test,
friedman, HSD.test, kruskal,
LSD.test, Median.test, PBIB.test,
REGW.test, SNK.test, waerden.test,
waller.test, plot.group
library(agricolae) data(sweetpotato) model<-aov(yield~virus, data=sweetpotato) comparison <- scheffe.test(model,"virus", group=TRUE,console=TRUE, main="Yield of sweetpotato\nDealt with different virus")#> #> Study: Yield of sweetpotato #> Dealt with different virus #> #> Scheffe Test for yield #> #> Mean Square Error : 22.48917 #> #> virus, means #> #> yield std r Min Max #> cc 24.40000 3.609709 3 21.7 28.5 #> fc 12.86667 2.159475 3 10.6 14.9 #> ff 36.33333 7.333030 3 28.0 41.8 #> oo 36.90000 4.300000 3 32.1 40.4 #> #> Alpha: 0.05 ; DF Error: 8 #> Critical Value of F: 4.066181 #> #> Minimum Significant Difference: 13.52368 #> #> Means with the same letter are not significantly different. #> #> yield groups #> oo 36.90000 a #> ff 36.33333 a #> cc 24.40000 ab #> fc 12.86667 b# Old version scheffe.test() df<-df.residual(model) MSerror<-deviance(model)/df Fc<-anova(model)["virus",4] out <- with(sweetpotato,scheffe.test(yield, virus, df, MSerror, Fc)) print(out)#> $statistics #> MSerror Df F Mean CV Scheffe CriticalDifference #> 22.48917 8 4.066181 27.625 17.1666 3.492641 13.52368 #> #> $parameters #> test name.t ntr alpha #> Scheffe virus 4 0.05 #> #> $means #> yield std r Min Max Q25 Q50 Q75 #> cc 24.40000 3.609709 3 21.7 28.5 22.35 23.0 25.75 #> fc 12.86667 2.159475 3 10.6 14.9 11.85 13.1 14.00 #> ff 36.33333 7.333030 3 28.0 41.8 33.60 39.2 40.50 #> oo 36.90000 4.300000 3 32.1 40.4 35.15 38.2 39.30 #> #> $comparison #> NULL #> #> $groups #> yield groups #> oo 36.90000 a #> ff 36.33333 a #> cc 24.40000 ab #> fc 12.86667 b #> #> attr(,"class") #> [1] "group"