# duncan.test: Duncan's new multiple range test In agricolae: Statistical Procedures for Agricultural Research

## Description

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.

## Usage

 `1` ```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

## Details

It is necessary first makes a analysis of variance.

## 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

## Author(s)

Felipe de Mendiburu

## 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.

`BIB.test`, `DAU.test`, `durbin.test`, `friedman`, `HSD.test`, `kruskal`, `LSD.test`, `Median.test`, `PBIB.test`, `REGW.test`, `scheffe.test`, `SNK.test`, `waerden.test`, `waller.test`, `plot.group`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```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") duncan.test(model,"virus",alpha=0.01,console=TRUE) # 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) print(out\$groups) ```

### Example output

```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
yield groups
oo 36.90000      a
ff 36.33333      a
cc 24.40000      b
fc 12.86667      c
```

agricolae documentation built on Sept. 13, 2017, 1:03 a.m.