scheffe.test: Multiple comparisons, scheffe

Description

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.

Usage

scheffe.test(y, trt, DFerror, MSerror, Fc, alpha = 0.05, group=TRUE, main = NULL,
console=FALSE )

Value

statistics: Statistics of the model
parameters: Design parameters
means: Statistical summary of the study variable
comparison: Comparison between treatments
groups: Formation of treatment groups

Arguments

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

Author

Felipe de Mendiburu

Details

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.

References

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

Examples

Run this code

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")
# 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)

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