ScottKnott-package(ScottKnott)
ScottKnott-package()所属R语言包：ScottKnott
The ScottKnott Clustering Algoritm
ScottKnott聚类导致算法性能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The Scott & Knott clustering algoritm is a very useful clustering algorithm widely used as a multiple comparison method in the Analysis of Variance context, as for example Gates and Bilbro (1978), Bony et al. (2001), Dilson et al. (2002) and Jyotsna et al. (2003).
斯科特和诺特聚类导致算法性能是一个非常有用的聚类算法广泛使用的多重比较法分析中的方差的情况下，例如盖茨和Bilbro的（1978年），骨等。 （2001），Dilson等。 （2002）和Jyotsna等。 （2003年）。
It was developed by Scott, A.J. and Knott, M. (Scott and Knott, 1974). All methods used up to that date as, for example, the t-test, Tukey, Duncan, Newman-Keuls procedures, have overlapping problems. By overlapping we mean the possibility of one or more treatments to be classified in more than one group, in fact, as the number of treatments reach a number of twenty or more, the number of overlappings could increse as reaching 5 or greater what makes almost impossible to the experimenter to really distinguish the real groups to which the means should belong. The Scott & Knott method does not have this problem, what is often cited as a very good quality of this procedure.
它是由斯科特，A.J.和Knott，M.（斯科特·诺特，1974年）。用完该日期的所有方法，例如，t-检验，杜克，邓肯，纽曼柯二氏程序，有重叠的问题。通过重叠，我们的意思是被归类在一个以上的基团中，实际上，作为治疗次数达到二十个或更多数目的可能性中的一个或多个处理，交替重迭，可适当增加数量达到大于或等于5什么使得几乎无法真正区分的手段应该是属于真正的群体，实验者。斯科特和诺特方法不会有这个问题，经常被援引作为此过程的质量非常好。
The Scott & Knott method make use of a clever algoritm of cluster analysis, where, starting from the the whole group of observed mean effects, it divides, and keep dividing the sub-groups in such a way that the intersection of any two groups formed in that manner is empty.
斯科特和诺特方法做一个聪明的使用导致算法性能的聚类分析，从整个集团的平均观察效果，它分为，并不断分裂，子组以这样一种方式，任何两个群体的交集以这种方式形成的，是空的。
Using their own words 'we study the consequences of using a well-known method of cluster analysis to partition the sample treatment means in a balanced design and show how a corresponding likelihood ratio test gives a method of judging the significance of difference among groups abtained'.
使用他们自己的话，我们研究的样本处理装置，在平衡的设计使用聚类分析的公知的方法进行分区的后果，并显示相应的似然比检验给出了一个判断基团从而得到满足一定之间的差异显着性的方法，怎样 。
Many studies, using the method of Monte Carlo, suggest that the Scott Knott method performs very well compared to other methods due to fact that it has high power and type I error rate almost always in accordance with the nominal levels. The ScottKnott package performs this algoritm starting either from vectors, matrices or data.frames joined as default, a aov or aovlist resulting object of previous analysis of variance. The results are given in the usual way as well as in graphical way using thermometers with diferent group colors.
，使用蒙特卡罗方法，许多研究表明，斯科特的诺特方法有很好的表现相比其他方法由于事实，即它具有高功率和I型错误率几乎总是在按照名义水平。 ScottKnott包执行这导致算法性能开始vectors，matrices或data.frames加入default，aov或aovlist以前生成的对象方差分析。结果给出在通常的方式，以及在图形化的方式使用采用几种不同的组颜色的温度计。
In a few words, the test of Scott & Knott is a clustering algoritm used as an one of the alternatives where multiple comparizon procedures are applied with a very important and almost unique characteristic: it does not present overlapping in the results.
几句话，斯科特·诺特是一个聚类导致算法性能测试使用作为一个多个comparizon一个非常重要的，几乎是独一无二的特性与应用程序的替代品，它不存在重叠的结果。
Details
详细信息----------Details----------
（作者）----------Author(s)----------
Enio Jelihovschi (<a href="mailto:eniojelihovs@gmail.com">eniojelihovs@gmail.com</a>)<br>
Jose Claudio Faria (<a href="mailto:joseclaudio.faria@gmail.com">joseclaudio.faria@gmail.com</a>)<br>
Sergio Oliveira (<a href="mailto:solive@uesc.br">solive@uesc.br</a>)<br>
参考文献----------References----------
Relationship be-tween Mycotoxin Synthesis and Isolate Morphology in Fungal Endophytes of Lolium perenne. New Phytologist, 1521, 125-137.
Scott-Knott, Tukey e Student-Newman-Keuls sob distribuicoes normal e nao normais dos residuos. Power and type I errors rate of Scott-Knott, Tukey and Student-Newman-Keuls tests under normal and no-normal distributions of the residues. Rev. Mat. Estat., Sao Paulo, 211: 67-83.
Testing. Bio-metrics, 411, 39-48.
error rates of Scott-Knotts test by the method of Monte Carlo. Cienc. agrotec., Lavras, 23, 687-696.
and selection of potatoes resistant to the US8 genotype of Phytophthora infestans from crosses between resistant and susceptible parents. Euphytica, 125, 129-138.
Separation. Agron J, 70, 462-465.
Series C (Applied Statistics), Vol. 22, No. 3, pp. 392-399.
Symbiotic Seed Germination and Mycorrhizae of Federally Threatened Platanthera PraeclaraOrchidaceae. American Midland Naturalist, 1491, 104-120.
e Melhoramento de Plantas. Editora UFLA.
analysis of variance. Biometrics, 30, 507-512.
实例----------Examples----------
##[＃]
## Examples: Completely Randomized Design (CRD)[＃示例：完全随机设计（CRD）]
## More details: demo(package='ScottKnott')[更多细节：演示（包=ScottKnott“的）]
##[＃]
## The parameters can be: vectors, design matrix and the response variable,[＃参数可以是：向量，设计矩阵和响应变量，]
## data.frame or aov[＃数据框或AOV]
data(CRD2)
## From: design matrix (dm) and response variable (y)[＃：设计矩阵（DM）和响应变量（Y）]
sk1 <- with(CRD2, SK(x=dm, y=y, model='y ~ x',
which='x', sig.level=0.005,
id.trim=5))
summary(sk1)
plot(sk1, col=rainbow(max(sk1$groups)), mm.lty=3, id.las=2, rl=FALSE,
title='Factor levels (sig.level=0.005)', )
## From: data.frame (dfm)[＃从：数据框设计（DFM）]
sk2 <- with(CRD2, SK(x=dfm, model='y ~ x',
which='x', id.trim=5))
summary(sk2)
plot(sk2, col=rainbow(max(sk2$groups)), id.las=2, rl=FALSE)
## From: aov[＃：AOV]
av <- with(CRD2, aov(y ~ x , data = dfm))
summary(av)
sk3 <- with(CRD2, SK(x=av,
which='x', id.trim=5))
summary(sk3)
plot(sk3, col=rainbow(max(sk3$groups)), rl=FALSE, id.las=2, title=NULL)
##[＃]
## Example: Randomized Complete Block Design (RCBD)[＃例如：随机区组设计（RCBD）]
## More details: demo(package='ScottKnott')[更多细节：演示（包=ScottKnott“的）]
##[＃]
## The parameters can be: design matrix and the response variable,[＃参数可以是：设计矩阵和响应变量，]
## data.frame or aov[＃数据框或AOV]
data(RCBD)
## Design matrix (dm) and response variable (y)[设计矩阵（DM）和响应变量（Y）]
sk1 <- with(RCBD, SK(x=dm, y=y, model='y ~ blk + tra',
which = 'tra'))
summary(sk1)
plot(sk1)
## From: data.frame (dfm), which='tra'[＃从数据框（DFM），=茶]
sk2 <- with(RCBD, SK(x=dfm, model='y ~ blk + tra',
which='tra'))
summary(sk2)
plot(sk2, mm.lty=3, title='Factor levels')
##[＃]
## Example: Latin Squares Design (LSD)[＃例如：拉丁方设计（LSD）]
## More details: demo(package='ScottKnott')[更多细节：演示（包=ScottKnott“的）]
##[＃]
## The parameters can be: design matrix and the response variable,[＃参数可以是：设计矩阵和响应变量，]
## data.frame or aov[＃数据框或AOV]
data(LSD)
## From: design matrix (dm) and response variable (y)[＃：设计矩阵（DM）和响应变量（Y）]
sk1 <- with(LSD, SK(x=dm, y=y, model='y ~ rows + cols + tra',
which='tra'))
summary(sk1)
plot(sk1)
## From: data.frame[＃从数据框]
sk2 <- with(LSD, SK(x=dfm, model='y ~ rows + cols + tra',
which='tra'))
summary(sk2)
plot(sk2, title='Factor levels')
## From: aov[＃：AOV]
av <- with(LSD, aov(y ~ rows + cols + tra, data=dfm))
summary(av)
sk3 <- SK(av,
which='tra')
summary(sk3)
plot(sk3, title='Factor levels')
##[＃]
## Example: Factorial Experiment (FE)[＃示例：因子实验（FE）]
## More details: demo(package='ScottKnott')[更多细节：演示（包=ScottKnott“的）]
##[＃]
## The parameters can be: design matrix and the response variable,[＃参数可以是：设计矩阵和响应变量，]
## data.frame or aov[＃数据框或AOV]
## Note: The factors are in uppercase and its levels in lowercase![＃注：这些因素是在大写字母和小写字母的水平！]
data(FE)
## From: design matrix (dm) and response variable (y)[＃：设计矩阵（DM）和响应变量（Y）]
## Main factor: N[＃主要因素：N]
sk1 <- with(FE, SK(x=dm, y=y, model='y ~ blk + N*P*K',
which='N'))
summary(sk1)
plot(sk1, title='Main effect: N')
## Nested: p1/N[＃嵌套：P1 / N]
nsk1 <- with(FE, SK.nest(x=dm, y=y, model='y ~ blk + N*P*K',
which='N ', fl2=1))
summary(nsk1)
plot(nsk1, title='Effect: p1/N')
## Nested: k1/P[＃嵌套：K1 / P]
nsk2 <- with(FE, SK.nest(x=dm, y=y, model='y ~ blk + N*P*K',
which='P:K', fl2=1))
summary(nsk2)
plot(nsk2, title='Effect: k1/P')
## Nested: k2/p2/N[＃嵌套：k2/p2/N]
nsk3 <- with(FE, SK.nest(x=dm, y=y, model='y ~ blk + N*P*K',
which='N:K', fl2=2, fl3=2))
summary(nsk3)
plot(nsk3, title='Effect: k2/p2/N')
## Nested: k1/n1/P[＃嵌套：k1/n1/P]
nsk4 <- with(FE, SK.nest(x=dm, y=y, model='y ~ blk + P*N*K',
which='P:N:K', fl2=1, fl3=1))
summary(nsk4)
plot(nsk4, title='Effect: k1/n1/P')
## Nested: p1/n1/K[＃嵌套：p1/n1/K]
nsk5 <- with(FE, SK.nest(x=dm, y=y, model='y ~ blk + K*N*P',
which='K:N', fl2=1, fl3=1))
summary(nsk5)
plot(nsk5, title='Effect: p1/n1/K')
##[＃]
## Example: Split-plot Experiment (SPE)[＃例如：裂区试验（SPE）]
## More details: demo(package='ScottKnott')[更多细节：演示（包=ScottKnott“的）]
##[＃]
## Note: The factors are in uppercase and its levels in lowercase![＃注：这些因素是在大写字母和小写字母的水平！]
data(SPE)
## The parameters can be: design matrix and the response variable,[＃参数可以是：设计矩阵和响应变量，]
## data.frame or aov[＃数据框或AOV]
## From: design matrix (dm) and response variable (y)[＃：设计矩阵（DM）和响应变量（Y）]
## Main factor: P[＃主因子：P]
sk1 <- with(SPE, SK(x=dm, y=y, model='y ~ blk + SP*P + Error(blk/P)',
which='P', error ='blk'))
summary(sk1)
plot(sk1)
## Main factor: SP[＃主要因素：SP]
sk2 <- with(SPE, SK(x=dm, y=y, model='y ~ blk + SP*P + Error(blk/P)',
which='SP', error ='Within', sig.level=0.025 ))
summary(sk2)
plot(sk2, xlab='Groups', ylab='Main effect: sp',
title='Main effect: SP (sig.level=0.025)')
## Nested: p1/SP[＃嵌套：p1/SP]
skn1 <- with(SPE, SK.nest(x=dm, y=y, model='y ~ blk + SP*P + Error(blk/P)',
which='SP', error ='Within', fl2=1 ))
summary(skn1)
plot(skn1, title='Effect: p1/SP')
##[＃]
## Example: Split-split-plot Experiment (SSPE)[＃例如：分割裂区试验（SSPE）]
## More details: demo(package='ScottKnott')[更多细节：演示（包=ScottKnott“的）]
##[＃]
## Note: The factors are in uppercase and its levels in lowercase![＃注：这些因素是在大写字母和小写字母的水平！]
data(SSPE)
## From: design matrix (dm) and response variable (y)[＃：设计矩阵（DM）和响应变量（Y）]
## Main factor: P[＃主因子：P]
sk1 <- with(SSPE, SK(dm, y, model='y ~ blk + SSP*SP*P + Error(blk/P/SP)',
which='P', error='blk'))
summary(sk1)
# Main factor: SP[主要因素：SP]
sk2 <- with(SSPE, SK(dm, y, model='y ~ blk + SSP*SP*P + Error(blk/P/SP)',
which='SP', error='blk:SP', sig.level=0.025))
summary(sk2)
# Main factor: SSP[主要因素：SSP]
sk3 <- with(SSPE, SK(dm, y, model='y ~ blk + SSP*SP*P + Error(blk/P/SP)',
which='SSP', error='Within', sig.level=0.1))
summary(sk3)
## Nested: p1/SP[＃嵌套：p1/SP]
skn1 <- with(SSPE, SK.nest(dm, y, model='y ~ blk + SSP*SP*P + Error(blk/P/SP)',
which='SP', error='blk:SP', fl2=1))
summary(skn1)
## From: aovlist[＃来自：aovlist的]
av <- with(SSPE, aov(y ~ blk + SSP*SP*P + Error(blk/P/SP), data=dfm))
summary(av)
## Nested: p/sp/SSP (at various levels of sp and p) [＃嵌套：P / SP / SSP（在不同级别的SP和P）]
skn6 <- SK.nest(av, which='SSP:SP', error='Within', fl2=1, fl3=1)
summary(skn6)
skn7 <- SK.nest(av, which='SSP:SP:P', error='Within', fl2=2, fl3=1)
summary(skn7)
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注：
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发表于 2012-9-29 22:59:10
