summary(TukeyC)
summary()所属R语言包：TukeyC
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Returns (and prints) a summary list for TukeyC objects.
的回报（打印）TukeyC对象的汇总列表。
用法----------Usage----------
## S3 method for class 'TukeyC'
summary(object,
参数----------Arguments----------
参数：object
A given object of the class TukeyC.
一个给定的对象的类TukeyC。
参数：complete
A logical value indicating if the summary is complete (mean difference and p-value) or only the groups.
一个逻辑值，如果是完整的总结（平均差异和p值），或仅组。
参数：...
Potential further arguments (required by generic). </table>
潜在的进一步参数（需要通用）。 </ 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----------
参见----------See Also----------
TukeyC
TukeyC
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)
##[＃]
## 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)
##[＃]
## 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)
##[＃]
## 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)
## Nested: k2/p2/N[＃嵌套：k2/p2/N]
ntk2 <- with(FE,
TukeyC.nest(x=dm,
y=y,
model='y ~ blk + N*P*K',
which='N:K',
fl2=2,
fl3=2))
summary(ntk2)
## Nested: k1/n1/P[＃嵌套：k1/n1/P]
ntk3 <- with(FE,
TukeyC.nest(x=dm,
y=y,
model='y ~ blk + P*N*K',
which='P:N:K',
fl2=1,
fl3=1))
summary(ntk3)
## Nested: p1/n1/K[＃嵌套：p1/n1/K]
ntk4 <- with(FE,
TukeyC.nest(x=dm,
y=y,
model='y ~ blk + K*N*P',
which='K:N',
fl2=1,
fl3=1))
summary(ntk4)
##[＃]
## Example: Split-plot Experiment (SPE)[＃例如：裂区试验（SPE）]
## More details: demo(package='TukeyC')[更多细节：演示（包=TukeyC“的）]
##[＃]
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]
tk1 <- with(SPE,
TukeyC(x=dm,
y=y,
model='y ~ blk + SP*P + Error(blk/P)',
which='P',
error='blk'))
summary(tk1)
## Nested: p1/SSP[＃嵌套：p1/SSP]
tkn1 <- with(SPE,
TukeyC.nest(x=dm,
y=y,
model='y ~ blk + SP*P + Error(blk/P)',
which='SP',
error='Within',
fl2=1 ))
summary(tkn1)
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]
tk1 <- with(SSPE,
TukeyC(dm,
y,
model='y ~ blk + SSP*SP*P + Error(blk/P/SP)',
which='P',
error='blk'))
summary(tk1)
# Main factor: SP[主要因素：SP]
tk2 <- with(SSPE,
TukeyC(dm,
y,
model='y ~ blk + SSP*SP*P + Error(blk/P/SP)',
which='SP',
error='blk:SP'))
summary(tk2)
# Main factor: SSP[主要因素：SSP]
tk3 <- with(SSPE,
TukeyC(dm,
y,
model='y ~ blk + SSP*SP*P + Error(blk/P/SP)',
which='SSP',
error='Within'))
summary(tk3)
## Nested: p1/SSP[＃嵌套：p1/SSP]
tkn1 <- with(SSPE,
TukeyC.nest(dm,
y,
model='y ~ blk + SSP*SP*P + Error(blk/P/SP)',
which='SP',
error='blk:SP',
fl2=1))
summary(tkn1)
## From: aovlist[＃来自：aovlist的]
av <- with(SSPE,
aov(y ~ blk + SSP*SP*P + Error(blk/P/SP),
data=dfm))
summary(av)
## Nested: pp/sp/SSP (at various levels of sp and p) [＃嵌套：PP / SP / SSP（在不同级别的SP和P）]
tkn2 <- TukeyC.nest(av,
which='SSP:SP',
error='Within',
fl2=1,
fl3=1)
summary(tkn2)
tkn3 <- TukeyC.nest(av,
which='SSP:SP:P',
error='Within',
fl2=2,
fl3=1)
summary(tkn3)
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注：
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发表于 2012-10-1 12:57:24
