agricolae (version 1.0-4)

waller.test: Multiple comparisons, Waller-Duncan

Description

The Waller-Duncan k-ratio t test is performed on all main effect means in the MEANS statement. See the K-RATIO option for information on controlling details of the test.

Usage

waller.test(y, trt, DFerror, MSerror, Fc, K = 100, group=TRUE, main = NULL)

Arguments

y

Variable response

trt

Treatments

DFerror

Degrees of freedom

MSerror

Mean Square Error

Fc

F Value

K

K-RATIO

group

TRUE or FALSE

main

Title

Value

• y
  Numeric
• trt
  factor
• DFerror
  Numeric
• MSerror
  Numeric
• Fc
  Numeric
• K
  Numeric
• group
  Logic
• main
  Text

Details

It is necessary first makes a analysis of variance. K-RATIO (K): value specifies the Type 1/Type 2 error seriousness ratio for the Waller-Duncan test. Reasonable values for KRATIO are 50, 100, and 500, which roughly correspond for the two-level case to ALPHA levels of 0.1, 0.05, and 0.01. By default, the procedure uses the default value of 100.

References

Waller, R. A. and Duncan, D. B. (1969). A Bayes Rule for the Symmetric Multiple Comparison Problem, Journal of the American Statistical Association 64, pages 1484-1504. Waller, R. A. and Kemp, K. E. (1976) Computations of Bayesian t-Values for Multiple Comparisons, Journal of Statistical Computation and Simulation, 75, pages 169-172. Steel & Torry & Dickey. Third Edition 1997 Principles and procedures of statistics a biometrical approach

See Also

HSD.test, LSD.test, bar.err, bar.group

Examples

library(agricolae)
data(sweetpotato)
attach(sweetpotato)
model<-aov(yield~virus)
df<-df.residual(model)
MSerror<-deviance(model)/df
Fc<-anova(model)[1,4]
comparison <- waller.test(yield, virus, df, MSerror, Fc, group=TRUE, 
main="Yield of sweetpotato. Dealt with different virus")
# std = F (default) is standard error
#startgraph
par(mfrow=c(2,2))
bar.err(comparison,std=TRUE,horiz=TRUE,xlim=c(0,45),density=4)
bar.err(comparison,std=TRUE,horiz=FALSE,ylim=c(0,45),density=8,col="blue")
bar.group(comparison,horiz=FALSE,ylim=c(0,45),density=8,col="red")
bar.group(comparison,horiz=TRUE,xlim=c(0,45),density=4,col="green")
#endgraph