Duncan's new multiple range test

This test is adapted from the Newman-Keuls method. Duncan's test does not control family wise error rate at the specified alpha level. It has more power than the other post tests, but only because it doesn't control the error rate properly. The Experimentwise Error Rate at: 1-(1-alpha)^(a-1); where "a" is the number of means and is the Per-Comparison Error Rate. Duncan's procedure is only very slightly more conservative than LSD. The level by alpha default is 0.05.

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

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	Degree free
MSerror	Mean Square Error
alpha	Significant level
group	TRUE or FALSE
main	Title
console	logical, print output

Value

statistics

Statistics of the model

parameters

Design parameters

duncan

Critical Range Table

means

Statistical summary of the study variable

comparison

Comparison between treatments

groups

Formation of treatment groups

Details

It is necessary first makes a analysis of variance.

if y = model, then to apply the instruction:
duncan.test(model, "trt", alpha = 0.05, group = TRUE, main = NULL, console = FALSE)
where the model class is aov or lm.

References

1. Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third Edition 1997
2. Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Examples


library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
out <- duncan.test(model,"virus",
main="Yield of sweetpotato. Dealt with different virus")
plot(out,variation="IQR")
#> Warning: NAs introduced by coercion
duncan.test(model,"virus",alpha=0.01,console=TRUE)
#> 
#> Study: model ~ "virus"
#> 
#> Duncan's new multiple range 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: 0.01 ; DF Error: 8 
#> 
#> Critical Range
#>        2        3        4 
#> 12.99223 13.52267 13.84424 
#> 
#> 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
# version old duncan.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
out <- with(sweetpotato,duncan.test(yield,virus,df,MSerror, group=TRUE))
plot(out,horiz=TRUE,las=1)
#> Warning: NAs introduced by coercion
print(out$groups)
#>       yield groups
#> oo 36.90000      a
#> ff 36.33333      a
#> cc 24.40000      b
#> fc 12.86667      c

Arguments

Value

Details

References

See also

Examples