summary(ScottKnott)
summary()所属R语言包：ScottKnott
Summary Method for SK and SK.nest Objects
总结方法SK和SK.nest对象
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Returns (and prints) a summary list for SK and SK.nest objects.
返回并打印一个汇总列表SK和SK.nest对象。
用法----------Usage----------
## S3 method for class 'SK'
summary(object, ...)
## S3 method for class 'SK.nest'
summary(object, ...)
参数----------Arguments----------
参数：object
A given object of the class SK or SK.nest.
一个给定的对象的类SK或SK.nest。
参数：...
Potential further arguments (require by generic).
潜在的进一步的论据（要求由通用）。
（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----------
Wadsworth & Brooks/Cole.
参见----------See Also----------
ScottKnott
ScottKnott
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)
##[＃]
## 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)
##[＃]
## 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)
##[＃]
## 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)
## 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)
## Nested: k2/p2/N[＃嵌套：k2/p2/N]
nsk2 <- with(FE, SK.nest(x=dm, y=y, model='y ~ blk + N*P*K',
which='N:K', fl2=2, fl3=2))
summary(nsk2)
## Nested: k1/n1/P[＃嵌套：k1/n1/P]
nsk3 <- with(FE, SK.nest(x=dm, y=y, model='y ~ blk + P*N*K',
which='P:N:K', fl2=1, fl3=1))
summary(nsk3)
## Nested: p1/n1/K[＃嵌套：p1/n1/K]
nsk4 <- with(FE, SK.nest(x=dm, y=y, model='y ~ blk + K*N*P',
which='K:N', fl2=1, fl3=1))
summary(nsk4)
##[＃]
## 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)
## 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)
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 /的SSP（在不同级别的SP和P）]
skn2 <- SK.nest(av, which='SSP:SP', error='Within', fl2=1, fl3=1)
summary(skn2)
skn3 <- SK.nest(av, which='SSP:SP:P', error='Within', fl2=2, fl3=1)
summary(skn3)
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注：
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发表于 2012-9-29 22:59:50
