SK(ScottKnott)
SK()所属R语言包：ScottKnott
The ScottKnott Clustering Algoritm for Single Experiments
单实验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----------
These are methods for objects of class vector, matrix or data.frame joined as default, aov and aovlist for single experiments.
这些类的对象的方法vector，matrix或data.frame加入默认情况下，aov和aovlist单一实验。
用法----------Usage----------
## Default S3 method:
SK(x, y=NULL, model, which, id.trim=3, error, sig.level=.05, ...)
## S3 method for class 'aov'
SK(x, which=NULL, id.trim=3, sig.level=.05, ...)
## S3 method for class 'aovlist'
SK(x, which, id.trim=3, error, sig.level=.05, ...)
参数----------Arguments----------
参数：x
A design matrix, data.frame or an aov object.
一个设计矩阵，data.frame或aov对象。
参数：y
A vector of response variable. It is necessary to inform this parameter only if x represent the design matrix.
一个向量的响应变量。这是必要的，告知该参数只有x设计矩阵。
参数：which
The name of the treatment to be used in the comparison. The name must be inside quoting marks.
要在比较中使用的处理的名称。该名称必须是内引用标记。
参数：model
If x is a data.frame object, the model to be used in the aov must be specified.
如果x是一个data.frame对象，该模型中要使用的AOV必须被指定。
参数：id.trim
The number of character to trim the id label.
字符到修剪的ID标签的数量。
参数：error
The error to be considered.
的错误加以考虑。
参数：sig.level
Level of Significance used in the SK algorithm to create the groups of means. The default value is 0.05.
水平的意义SK算法创建组的方式。默认值是0.05。
参数：...
Potential further arguments (require by generic).
潜在的进一步的论据（要求由通用）。
Details
详细信息----------Details----------
The function SK returns an object of class SK respectivally containing the groups of means plus other necessary variables for summary and plot.
的功能SK返回一个类的对象SK的respectivally包含组汇总和图，加上其他必要的变量。
The generic functions summary and plot are used to obtain and print a summary and a plot of the results.
通用功能中的summary和plot是用来获取和打印的总结和图的结果。
值----------Value----------
The function SK returns a list of the class SK with the slots:
的功能SK返回一个列表之类的SK的插槽：
参数：av
A list storing the result of aov.
Alist存储的结果aov。
参数：groups
A vector of length equal the number of factor levels marking the groups generated.
甲向量，长度等于因子水平标记产生的基团的数目。
参数：nms
A vector of the labels of the factor levels.
因子水平的标签的向量。
参数：ord
A vector which keeps the position of the means of the factor levels in decreasing order.
一种向量，保持位置的因子水平的方法，以递减的顺序。
参数：m.inf
A matrix which keeps the means, minimum and maximum of the factor levels in decreasing order.
这使的手段，最小和最大的因素水平的递减顺序的矩阵。
参数：sig.level
A vector of length 1 giving the level of significance of the test.
长度为1的一种向量，给测试的重要性的电平。
（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----------
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')
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-9-29 22:59:28
