zwdbordeaux
如果你实在想用邓肯多重比较,请看下面的信息。我在包agricolae中看到一个多重比较的方法,waller-Duncan,不知道是否就是你说的!
waller.test(agricolae) R Documentation
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
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.
Value
y Numeric
trt factor
DFerror Numeric
MSerror Numeric
Fc Numeric
K Numeric
group Logic
main Text
Author(s)
Felipe de Mendiburu
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