TukeyC(TukeyC)
TukeyC()所属R语言包：TukeyC
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
这些方法的对象类vector，matrix或data.frame加入为默认值，aov和aovlist
用法----------Usage----------
## Default S3 method:
TukeyC(x,
y=NULL,
model,
which,
error,
sig.level=.05,
round=2, ...)
## S3 method for class 'aov'
TukeyC(x,
which=NULL,
sig.level=.05,
round=2, ...)
## S3 method for class 'aovlist'
TukeyC(x,
which,
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：error
The error to be considered.
的错误加以考虑。
参数：sig.level
Level of Significance used in the TukeyC algorithm to create the groups of means. The default value is 0.05.
等级使用的意义在TukeyC算法创建装置的基团。默认值是0.05。
参数：round
Integer indicating the number of decimal places.
整数，表示小数位数。
参数：...
Potential further arguments (required by generic). </table>
潜在的进一步参数（需要通用）。 </ TABLE>
Details
详细信息----------Details----------
The function TukeyC returns an object of class TukeyC respectivally containing the groups of means plus other necessary variables for summary and plot.
的功能TukeyC返回一个类的对象TukeyC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
的通用功能summary和plot是用来获取和
值----------Value----------
The function TukeyC returns a list of the class TukeyC with the slots:
的功能TukeyC返回一个列表之类的TukeyC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. </table>
长度为1的一种向量，给测试的重要性的电平。 </ TABLE>
（作者）----------Author(s)----------
Jose Claudio Faria (<a href="mailto:joseclaudio.faria@gmail.com">joseclaudio.faria@gmail.com</a>)<br>
Enio Jelihovschi (<a href="mailto:eniojelihovs@gmail.com">eniojelihovs@gmail.com</a>)<br>
Ivan Bezerra Allaman (<a href="mailto:ivanalaman@gmail.com">ivanalaman@gmail.com</a>)
参考文献----------References----------
e Melhoramento de Plantas. Editora UFLA.
a biometrical approach. Third Edition.
实例----------Examples----------
##[＃]
## Examples: Completely Randomized Design (CRD)[＃示例：完全随机设计（CRD）]
## More details: demo(package='TukeyC')[更多细节：演示（包=TukeyC“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）]
tk1 <- with(CRD2,
TukeyC(x=dm,
y=y,
model='y ~ x',
which='x',
id.trim=5))
summary(tk1)
## From: data.frame (dfm)[＃从：数据框设计（DFM）]
tk2 <- with(CRD2,
TukeyC(x=dfm,
model='y ~ x',
which='x',
id.trim=5))
summary(tk2)
## From: aov[＃：AOV]
av <- with(CRD2,
aov(y ~ x,
data=dfm))
summary(av)
tk3 <- with(CRD2,
TukeyC(x=av,
which='x',
id.trim=5))
summary(tk3)
##[＃]
## Example: Randomized Complete Block Design (RCBD)[＃例如：随机区组设计（RCBD）]
## More details: demo(package='TukeyC')[更多细节：演示（包=TukeyC“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）]
tk1 <- with(RCBD,
TukeyC(x=dm,
y=y,
model='y ~ blk + tra',
which='tra'))
summary(tk1)
## From: data.frame (dfm), which='tra'[＃从数据框（DFM），=茶]
tk2 <- with(RCBD,
TukeyC(x=dfm,
model='y ~ blk + tra',
which='tra'))
summary(tk2)
##[＃]
## Example: Latin Squares Design (LSD)[＃例如：拉丁方设计（LSD）]
## More details: demo(package='TukeyC')[更多细节：演示（包=TukeyC“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）]
tk1 <- with(LSD,
TukeyC(x=dm,
y=y,
model='y ~ rows + cols + tra',
which='tra'))
summary(tk1)
## From: data.frame[＃从数据框]
tk2 <- with(LSD,
TukeyC(x=dfm,
model='y ~ rows + cols + tra',
which='tra'))
summary(tk2)
## From: aov[＃：AOV]
av <- with(LSD,
aov(y ~ rows + cols + tra,
data=dfm))
summary(av)
tk3 <- TukeyC(av,
which='tra')
summary(tk3)
##[＃]
## Example: Factorial Experiment (FE)[＃示例：因子实验（FE）]
## More details: demo(package='TukeyC')[更多细节：演示（包=TukeyC“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(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]
tk1 <- with(FE,
TukeyC(x=dm,
y=y,
model='y ~ blk + N*P*K',
which='N'))
summary(tk1)
## Nested: p1/N[＃嵌套：P1 / N]
ntk1 <- with(FE,
TukeyC.nest(x=dm,
y=y,
model='y ~ blk + N*P*K',
which='N ',
fl2=1))
summary(ntk1)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-10-1 12:57:53
