TukeyC (version 1.1-5)

summary: Summary Method for TukeyC and TukeyC.nest Objects

Description

Returns (and prints) a summary list for TukeyC objects.

Usage

# S3 method for TukeyC
summary(object,
        complete=TRUE, …)

Arguments

object

A given object of the class TukeyC.

complete

A logical value indicating if the summary is complete (mean difference and p-value) or only the groups.

…

Potential further arguments (required by generic).

References

Chambers, J.M. & Hastie, T.J. (1992) Statistical Models in S. Wadsworth & Brooks/Cole.

See Also

TukeyC

Examples

# NOT RUN {
  ##
  ## Examples: Completely Randomized Design (CRD)
  ## More details: demo(package='TukeyC')
  ##
  
  ## The parameters can be: vectors, design matrix and the response variable,
  ## data.frame or aov
  data(CRD2)
  
  ## From: design matrix (dm) and response variable (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)
  ## More details: demo(package='TukeyC')
  ##
  
  ## The parameters can be: design matrix and the response variable,
  ## data.frame or aov
  
  data(RCBD)
  
  ## Design matrix (dm) and response variable (y)
  tk1 <- with(RCBD,
              TukeyC(x=dm,
                     y=y,
                     model='y ~ blk + tra',
                     which='tra'))
  summary(tk1)

  ##
  ## Example: Latin Squares Design (LSD)
  ## More details: demo(package='TukeyC')
  ##
  
  ## The parameters can be: design matrix and the response variable,
  ## data.frame or aov
       
  data(LSD)
  
  ## From: design matrix (dm) and response variable (y)
  tk1 <- with(LSD,
              TukeyC(x=dm,
                     y=y,
                     model='y ~ rows + cols + tra',
                     which='tra'))
  summary(tk1)

  ##
  ## Example: Factorial Experiment (FE)
  ## More details: demo(package='TukeyC')
  ##
  
  ## The parameters can be: design matrix and the response variable,
  ## data.frame or aov
  
  data(FE)
  ## From: design matrix (dm) and response variable (y)
  ## Main factor: N
  tk1 <- with(FE,
              TukeyC(x=dm,
                     y=y,
                     model='y ~ blk + N*P*K', 
                     which='N'))
  summary(tk1)

  ## Nested: p1/N
  ## Testing N inside of level one of P
  ntk1 <- with(FE,
               TukeyC.nest(x=dm,
                           y=y,
                           model='y ~ blk + N*P*K',
                           which='P:N',
                           fl1=1))
  summary(ntk1)

  ## Nested: k1/p1/N
  ## Testing N inside of level one of K and level one of P
  ntk2 <- with(FE,
               TukeyC.nest(x=dm,
                           y=y,
                           model='y ~ blk + N*P*K',
                           which='K:P:N',
                           fl1=1,
                           fl2=1))
  summary(ntk2)

  ## Nested: k2/n2/P
  ntk3 <- with(FE,
               TukeyC.nest(x=dm,
                           y=y,
                           model='y ~ blk + N*P*K',
                           which='K:N:P',
                           fl1=2, 
                           fl2=2))
  summary(ntk3)

  ## Nested: p1/n1/K
  ntk4 <- with(FE,
               TukeyC.nest(x=dm,
                           y=y,
                           model='y ~ blk + N*P*K',
                           which='P:N:K',
                           fl1=1,
                           fl2=1))
  summary(ntk4)

  ##
  ## Example: Split-plot Experiment (SPE)
  ## More details: demo(package='TukeyC')
  ##
  
  data(SPE)

  ## The parameters can be: design matrix and the response variable,
  ## data.frame or aov
  
  ## From: design matrix (dm) and response variable (y)
  ## Main factor: P
  tk1 <- with(SPE,
              TukeyC(x=dm,
                     y=y,
                     model='y ~ blk + P*SP + Error(blk/P)',
                     which='P',
                     error='blk:P'))
  summary(tk1)

  ## Nested: p1/SP
  tkn1 <- with(SPE,
               TukeyC.nest(x=dm,
                           y=y,
                           model='y ~ blk + P*SP + Error(blk/P)',
                           which='P:SP',
                           error='Within',
                           fl1=1 ))
  summary(tkn1)

  data(SSPE)

  ## From: design matrix (dm) and response variable (y)
  ## Main factor: P
  tk1 <- with(SSPE,
              TukeyC(dm,
                     y,
                     model='y ~ blk + P*SP*SSP + Error(blk/P/SP)',
                     which='P',
                     error='blk:P'))
  summary(tk1)
  
  # Main factor: SP
  tk2 <- with(SSPE,
              TukeyC(dm,
                     y,
                     model='y ~ blk + P*SP*SSP + Error(blk/P/SP)',
                     which='SP',
                     error='blk:P:SP'))
  summary(tk2)
  
  # Main factor: SSP
  tk3 <- with(SSPE,
              TukeyC(dm,
                     y,
                     model='y ~ blk + P*SP*SSP + Error(blk/P/SP)',
                     which='SSP',
                     error='Within'))
  summary(tk3)
  
  ## Nested: p1/SSP
  tkn1 <- with(SSPE,
               TukeyC.nest(dm,
                           y,
                           model='y ~ blk + P*SP*SSP + Error(blk/P/SP)',
                           which='P:SSP',
                           error='blk:P:SP',
                           fl1=1))
  summary(tkn1)

  ## From: aovlist
  av <- with(SSPE,
             aov(y ~  blk + P*SP*SSP + Error(blk/P/SP),
             data=dfm))
  summary(av)   

  ## Nested: P1/SP1/SSP
  tkn2 <- TukeyC.nest(av,
                      which='P:SP:SSP',
                      error='Within', 
                      fl1=1,
                      fl2=1)
  summary(tkn2)

  ## Nested: P2/SP1/SSP
  tkn3 <- TukeyC.nest(av,
                      which='P:SP:SSP',
                      error='Within',
                      fl1=2,
                      fl2=1)
  summary(tkn3)
# }