Package {TukeyC}

Conventional Tukey Test

Description

This package performs what is known as the Tukey HSD test in the conventional way. It also uses an algorithm which divides the set of all means in groups and assigns letters to the different groups, allowing for overlapping. This is done for simple experimental designs and schemes. The most usual designs are: Completely Randomized Design (‘⁠CRD⁠’), Randomized Complete Block Design (‘⁠RCBD⁠’) and Latin Squares Design (‘⁠LSD⁠’). The most usual schemes are: Factorial Experiment (‘⁠FE⁠’), Split-Plot Experiment (‘⁠SPE⁠’) and Split-Split-Plot Experiment (‘⁠SSPE⁠’).

The package can be used for balanced and unbalanced experiments (when possible).

R has some functions (TukeyHSD provided by stats, glht provided by multcomp, HSD.test provided by agricolae and cld provided by multcomp) that also perform the Tukey test. The TukeyHSD returns intervals based on the range of the sample means rather than the individual differences. Those intervals are based on Studentized range statistics and are, in essence, confidence intervals. This approach has two advantages: p-values are shown, allowing the user to allow flexible inferential decisions and make it possible to plot the result of the test. However, it has one disadvantage, since the final result is more difficult to understand and summarize. Others (glht, cld) are also useful but difficult to manage. Additionally, many users of other statistical software are accustomed to letters that group the means of the factor tested, which makes it unattractive or difficult to adapt to common R workflows.

So, the main aim of this package is to make available in the R environment the conventional approach to the Tukey test with a set of flexible functions and S3 methods.

Author(s)

Faria, J. C. (joseclaudio.faria@gmail.com)
Jelihovschi, E. G. (eniojelihovs@gmail.com)
Allaman, I. B. (ivanalaman@gmail.com)

References

Miller, R.G. (1981) Simultaneous Statistical Inference. Springer.

Ramalho M.A.P, Ferreira D.F and Oliveira A.C. (2000) Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Steel, R.G., Torrie, J.H and Dickey D.A. (1997) Principles and procedures of statistics: a biometrical approach. Third Edition.

Yandell, B.S. (1997) Practical Data Analysis for Designed Experiments. Chapman & Hall.

Completely Randomized Design (CRD)

Description

A list illustrating the resources of TukeyC package related to Completely Randomized Design (‘⁠CRD⁠’).

Usage

  data(CRD1)

Details

A simulated data to model a Completely Randomized Design (‘⁠CRD⁠’) of 4 factor levels and 6 repetitions.

Completely Randomized Design (‘⁠CRD⁠’)

Description

A list illustrating the resources of TukeyC package related to Completely Randomized Design (‘⁠CRD⁠’).

Usage

  data(CRD2)

Details

A simulated data to model a Completely Randomized Design (‘⁠CRD⁠’) of 45 factor levels and 4 repetitions.

Factorial Experiment (FE)

Description

A list illustrating the resources of TukeyC package related to Factorial Experiment (‘⁠FE⁠’).

Usage

  data(FE)

Details

A simulated data to model a Factorial Experiment (‘⁠FE⁠’) with 3 factors, 2 levels per factor and 4 blocks.

Latin Squares Design (LSD)

Description

A list illustrating the resources of TukeyC package related to Latin Squares Design (‘⁠LSD⁠’).

Usage

  data(LSD)

Details

A simulated data to model a Latin Squares Design (‘⁠LSD⁠’) with 5 5 treatment levels, 5 rows, and 5 columns.

Randomized Complete Block Design (RCBD)

Description

A list illustrating the resources of TukeyC package related to Randomized Complete Block Design (‘⁠RCBD⁠’).

Usage

  data(RCBD)

Details

A simulated data to model a Randomized Complete Block Design (‘⁠RCBD⁠’) of 5 factor levels, 4 blocks and 4 one replicate of each treatment per block.

Split-plot Experiment (SPE)

Description

A list to illustrate the resources of TukeyC package related to Split-plot Experiment (‘⁠SPE⁠’).

Usage

  data(SPE)

Details

A simulated data to model a Split-plot Experiment (‘⁠SPE⁠’) with 3 plots, each one split 4 times and 6 repetitions per split.

Split-plot Experiment in Time (SPET)

Description

The experiment consists of 8 treatments (7 leguminous cover crops and maize) in a Randomized Complete Block Design (‘⁠RCBD⁠’) and the yield by plot (kg/plot).

Usage

  data(SPET)

Source

Gomes, F.P. (1990). Curso de Estatistica Experimental. 13 ed. Editora NOBEL, Piracicaba, Brazil, page 157.

Split-split-plot Experiment (SSPE)

Description

A list to illustrate the resources of TukeyC package related to Split-split-plot Experiment (‘⁠SSPE⁠’).

Usage

  data(SSPE)

Details

A simulated data to model a Split-split-plot Experiment (‘⁠SSPE⁠’) with 3 plots, each one split 3 times, each split, split again 5 times and 4 repetitions per split-split.

The TukeyC Test for Single Experiments

Description

These are methods for objects of class formula, lm, aov, aovlist and lmerMod for single, factorial, split-plot and split-split-plot experiments.

Usage

TukeyC(x,...)

## S3 method for class 'formula'
TukeyC(formula,
       data            = NULL,
       which           = NULL,
       fl1             = NULL,
       fl2             = NULL,
       error           = NULL,
       sig.level       = .05,
       round           = 2,
       adjusted.pvalue = 'none',
       ...)

## S3 method for class 'lm'
TukeyC(x,
       which           = NULL,
       fl1             = NULL,
       fl2             = NULL,
       error           = NULL,
       sig.level       = .05,
       round           = 2,
       adjusted.pvalue = 'none',
       ...)

## S3 method for class 'aovlist'
TukeyC(x,
       which           = NULL,
       fl1             = NULL,
       fl2             = NULL,
       error           = NULL,
       sig.level       = .05,
       round           = 2,
       adjusted.pvalue = 'none',
       ...)

## S3 method for class 'lmerMod'
TukeyC(x,
       which           = NULL,
       fl1             = NULL,
       fl2             = NULL,
       error           = NULL,
       sig.level       = .05,
       round           = 2,
       adjusted.pvalue = 'none',
       ...)

Arguments

Details

The function TukeyC returns an object of class TukeyC containing the groups of means plus other necessary variables for summary and plot.

The generic functions summary and plot are used to obtain and print a summary and a plot of the results.

The error arguments may be used when the user wants a specific error other than the experimental error. In split-plot and split-split-plot designs, error terms may be combined with "/" in the sequence of the which argument. For example, an aovlist object, a possible combination would be error = 'Within/blk:plot' in a blocked split-plot design with which = 'subplot:plot' argument.

Value

The function TukeyC returns a list of the class TukeyC with the slots:

Author(s)

Faria, J. C. (joseclaudio.faria@gmail.com)
Jelihovschi, E. G. (eniojelihovs@gmail.com)
Allaman, I. B. (ivanalaman@gmail.com)

References

Miller, R.G. (1981) Simultaneous Statistical Inference. Springer.

Ramalho M.A.P, Ferreira D.F and Oliveira A.C. (2000) Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Steel, R.G., Torrie, J.H and Dickey D.A. (1997) Principles and procedures of statistics: a biometrical approach. Third Edition.

Yandell, B.S. (1997) Practical Data Analysis for Designed Experiments. Chapman and Hall.

Examples

##
## Examples: Randomized Complete Block Design (RCBD)
## More details: demo(package='TukeyC')
##

## The parameters can be: formula, aov, lm, aovlist and lmerMod

data(RCBD)

## From: formula
tk1 <- with(RCBD,
            TukeyC(y ~ blk + tra,
                   data=dfm,
                   which='tra'))
summary(tk1)

## From: lmerMod
## This class is specific of the lme4 package.

  if(require(lme4)){
    lmer1 <- with(RCBD,
                  lmer(y ~ (1|blk) + tra,
                       data=dfm))

    tk2 <-  TukeyC(lmer1,
                   which='tra')
    summary(tk2)
  }

##
## Example: Latin Squares Design (LSD)
## More details: demo(package='TukeyC')
##

data(LSD)

## From: formula
tk3 <- with(LSD,
            TukeyC(y ~ rows + cols + tra,
                   data=dfm,
                   which='tra'))
summary(tk3)

## From: aov
av1 <- with(LSD,
            aov(y ~ rows + cols + tra,
                data=dfm))

tk4 <- TukeyC(av1,
              which='tra')
summary(tk4)

## From: lm
lm1 <- with(LSD,
            lm(y ~ rows + cols + tra,
               data=dfm))

tk5 <- TukeyC(lm1,
              which='tra')
summary(tk5)

##
## Example: Factorial Experiment (FE)
## More details: demo(package='TukeyC')
##

data(FE)
## From: formula
## Main factor: N
tk6 <- with(FE,
            TukeyC(y ~ blk + N*P*K,
                   data=dfm,
                   which='N'))
summary(tk6)

## Nested: p1/N
# From: formula
n_tk1 <- with(FE,
              TukeyC(y ~ blk + N*P*K,
                     data=dfm,
                     which='P:N',
                     fl1=1))
summary(n_tk1) 

## Nested: p2/N
# From: lm
lm2 <- with(FE,
            lm(y ~ blk + N*P*K, 
               dfm))

n_tk2 <- with(FE,
              TukeyC(lm2,
                     which='P:N',
                     fl1=2))
summary(n_tk2) 

## Nested: n1/P
# From: aov
av2 <- with(FE,
            aov(y ~ blk + N*P*K,
                dfm))

n_tk3 <- with(FE,
              TukeyC(av2,
                     which='N:P',
                     fl1=1))
summary(n_tk3) 

# From: lmerMod

  if(require(lme4)){
    lmer2 <- with(FE,
                  lmer(y ~ (1|blk) + N*P*K,
                       dfm))

    n_tk4 <- with(FE,
                  TukeyC(lmer2,
                         which='N:P',
                         fl1=1))
    summary(n_tk4)
  }


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

## From lm
lm3 <- with(SPET,
            lm(y ~ blk*tra + tra*year,
               dfm))

# crotjuncea/year
sp_tk1 <- TukeyC(lm3,
                 which='tra:year',
                 fl1=1)
summary(sp_tk1) 

# year1/tra
# Specify the year error combined with the treatment error in the order of the which argument.
sp_tk2 <-  TukeyC(lm3,
                  which='year:tra',
                  error='Residuals/blk:tra',
                  fl1=1)
summary(sp_tk2)

# From: lmerMod
# Only tra

  if(require(lme4)){
    lmer3 <- with(SPET,
                  lmer(y ~ blk + (1|blk:tra) + tra*year,
                       dfm))

    # comparison only tra
    sp_tk3 <- TukeyC(lmer3,
                     which = 'tra',
                     error = 'blk:tra')
    summary(sp_tk3)

    # year1/tra
    sp_tk4 <- TukeyC(lmer3,
                     which='year:tra',
                     error='Residual/blk:tra',
                     fl1=1)
    summary(sp_tk4)
  }


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

data(SSPE)
## From: formula
## Main factor: P
## Specify the appropriate error term
ssp_tk1 <- with(SSPE,
                TukeyC(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                       data=dfm,
                       which='P',
                       error='blk:P'))
summary(ssp_tk1)

## Main factor: SP
## Specify the appropriate error term
ssp_tk2 <- with(SSPE,
                TukeyC(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                       data=dfm,
                       which='SP',
                       error='blk:P:SP'))
summary(ssp_tk2)

## Main factor: SSP
ssp_tk3 <- with(SSPE,
                TukeyC(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                       data=dfm,
                       which='SSP'))
summary(ssp_tk3)

## From: aov
## Main factor: SSP
av3 <- with(SSPE,
            aov(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                data=dfm))

ssp_tk4 <- TukeyC(av3,
                  which='SSP')
summary(ssp_tk4)

## Nested: p1/SP
## Specify the appropriate error term
ssp_tk5 <- TukeyC(av3,
                  which='P:SP',
                  error='blk:P:SP',
                  fl1=1)
summary(ssp_tk5)

## Nested: p1/SSP
ssp_tk6 <- TukeyC(av3,
                  which='P:SSP',
                  fl1=1)
summary(ssp_tk6)

## Nested: p1/sp1/SSP
## Testing SSP inside of level one of P and level one of SP
ssp_tk7 <- TukeyC(av3,
                  which='P:SP:SSP',
                  fl1=1,
                  fl2=1)
summary(ssp_tk7)

## Nested: p2/sp1/SSP
ssp_tk8 <- TukeyC(av3,
                  which='P:SP:SSP',
                  fl1=2,
                  fl2=1)
summary(ssp_tk8)

## Nested: sp1/P
## Specify the appropriate error term
ssp_tk9 <- TukeyC(av3,
                  which='SP:P',
                  error='blk:P:SP/blk:P',
                  fl1=1)

summary(ssp_tk9)

## Nested: ssp1/SP
ssp_tk10 <- TukeyC(av3,
                   which='SSP:SP',
                   error='Within/blk:P:SP',
                   fl1=1)
summary(ssp_tk10)

## Nested: ssp1/sp1/P
## Specify the appropriate error term
ssp_tk11 <- TukeyC(av3,
                   which='SSP:SP:P',
                   error='Within/blk:P:SP/blk:P',
                   fl1=1,
                   fl2=1)
summary(ssp_tk11)

## UNBALANCED DATA
## Means are adjusted using least-squares means.
## From: formula
data(CRD2)

uCRD2 <- CRD2$dfm
uCRD2[c(3, 5, 10, 44, 45), 3] <- NA

utk1 <-  TukeyC(y ~ x,
                data=uCRD2,
                which='x')
summary(utk1)

## From: lm
ulm1 <- lm(y ~ x,
           data=uCRD2)

utk2 <- TukeyC(ulm1,
               which='x')
summary(utk2)


## Factorial Experiments
## Nested: p1/N
# From: lm

uFE <- FE$dfm
uFE[c(3, 6, 7, 20, 31, 32), 5] <- NA

ulm2 <- lm(y ~ blk + N*P*K,
           uFE)

## Nested: p1/N
utk3 <- TukeyC(ulm2,
               data=uFE,
               which='P:N',
               fl1=1)
summary(utk3) 

## Nested: p2/n2/K
utk4 <- TukeyC(ulm2,
               data=uFE,
               which='P:N:K',
               fl1=2,
               fl2=2)
summary(utk4) 

Internal TukeyC functions

Description

Internal TukeyC functions.

Details

These are not to be called by the user and are undocumented.

Boxplot TukeyC Objects

Description

S3 method to plot TukeyC objects.

Usage

## S3 method for class 'TukeyC'
boxplot(x,
          mean.type   = c('line', 'point', 'none'),
          xlab        = NULL,
          mean.col    = 'gray',
          mean.pch    = 1,
          mean.lwd    = 1,
          mean.lty    = 1,
          args.legend = NULL, ...)

Arguments

Details

The boxplot.TukeyC function is an S3 method to plot TukeyC objects. Unlike the generic boxplot, it displays Tukey group letters and overlays treatment means on the boxes.

Value

'NULL' (invisibly). The main purpose of this function is to produce a plot.

Author(s)

Faria, J. C. (joseclaudio.faria@gmail.com)
Jelihovschi, E. G. (eniojelihovs@gmail.com)
Allaman, I. B. (ivanalaman@gmail.com)

References

Murrell, P. (2005) R Graphics. Chapman and Hall/CRC Press.

See Also

boxplot

Examples

##
## Examples: Completely Randomized Design (CRD)
## More details: demo(package='TukeyC')
##

library(TukeyC)
data(CRD1)

## From: formula
# Simple!
tk1 <- TukeyC(y ~ x,
              data=CRD1$dfm,
              which='x')
boxplot(tk1)

# A little more elaborate!
boxplot(tk1,
        mean.lwd=1.3,
        mean.col='red')

# A little more! 
boxplot(tk1,
        mean.lwd=1.3,
        mean.lty=2,
        mean.col='red',
        args.legend=list(x='bottomleft'))

# With point type!
boxplot(tk1,
        mean.type='point')

boxplot(tk1,
        mean.type='point',
        mean.pch=19,
        cex=1.5,
        mean.col='red')

# With other point
boxplot(tk1,
        mean.type='point',
        mean.pch='+',
        cex=2,
        mean.col='blue',
        args.legend=list(x='bottomleft'))

Coefficient of variation

Description

Returns the coefficient of variation from models lm, aov and aovlist.

Usage

cv(x, 
   round=2)

Arguments

Details

sqrt(MSerror)*100/mean(x)

Value

Author(s)

Faria, J. C. (joseclaudio.faria@gmail.com)
Jelihovschi, E. G. (eniojelihovs@gmail.com)
Allaman, I. B. (ivanalaman@gmail.com)

Examples

library(TukeyC)

## Completely Randomized Design (CRD - aov) 
data(CRD1)
av1 <- with(CRD1,
            aov(y ~ x,
                data=dfm))
summary(av1)
cv(av1)

## Randomized Complete Block Design (RCBD - aov)
data(RCBD)
av2 <- with(RCBD,
            aov(y ~ blk + tra,
                data=dfm))
summary(av2)
cv(av2)

## Split-plot experiment (SPE - aovlist)
data(SPE)
av3 <- with(SPE,
            aov(y ~ blk + P*SP + Error(blk/P),
                data=dfm))
summary(av3)
cv(av3)

## Split-split-plot experiment (SSPE - aovlist)
data(SSPE)
av4 <- with(SSPE,
            aov(y ~  blk + P*SP*SSP + Error(blk/P/SP),
                data=dfm))
summary(av4)
cv(av4)

## storing
res <- cv(av4)
res[2:3]

Make Tukey Groups

Description

Builds compact letter groups from the full pairwise significance matrix produced by the Tukey test.

Usage

make.TukeyC.groups(x)

Arguments

Details

The algorithm scans each treatment, forms candidate groups of non-significant comparisons, enforces internal consistency when pairwise patterns are non-transitive (as in severely unbalanced designs), removes redundant subsets, and writes the familiar column-wise letter display (G1, G2, ...).

Value

A character matrix with row names equal to the treatment levels (sorted by decreasing mean) and one column per letter group (G1, G2, ...). Empty cells indicate that the treatment does not carry that letter.

Note

This function is for internal use in the TukeyC package.

Author(s)

Faria, J. C. (joseclaudio.faria@gmail.com)
Jelihovschi, E. G. (eniojelihovs@gmail.com)
Allaman, I. B. (ivanalaman@gmail.com)

Make Tukey Test

Description

Performs all pairwise Tukey comparisons for balanced or unbalanced designs and assembles the result table with letter groups.

Usage

make.TukeyC.test(obj,
                 MSE,
                 sig.level,
                 dfr,
                 round,
                 adjusted.pvalue)

Arguments

Value

A list with:

Result

data.frame with column Means and letter columns G1, G2, ...

Sig.level

Significance level used.

Diff_Prob

Matrix of mean differences (upper triangle) and adjusted p-values (lower triangle).

MSD

Minimum significant differences for each pair.

Replicates

Replicate counts per level.

Note

This function is for internal use in the TukeyC package.

Author(s)

Faria, J. C. (joseclaudio.faria@gmail.com)
Jelihovschi, E. G. (eniojelihovs@gmail.com)
Allaman, I. B. (ivanalaman@gmail.com)

Plot TukeyC and TukeyC.nest Objects

Description

S3 method to plot TukeyC and TukeyC.nest objects.

Usage

## S3 method for class 'TukeyC'
plot(x,
     result         = TRUE,
     replicates     = TRUE,
     pch            = 19,
     col            = NULL,
     xlab           = NULL,
     ylab           = NULL,
     xlim           = NULL,
     ylim           = NULL,
     axisx          = TRUE,
     axisy          = TRUE,
     id.lab         = NULL,
     id.las         = 1,
     yl             = TRUE,
     yl.lty         = 3,
     yl.col         = 'gray',
     dispersion     = c('mm','sd','ci','cip'),
     d.lty          = 1,
     d.col          = 'black',
     title          = '', ...)

Arguments

Details

The plot.TukeyC function is an S3 method to plot TukeyC and TukeyC.nest objects. It generates a series of points (the means) and a vertical line showing the dispersion of the values corresponding to each group mean. With dispersion = "ci", intervals use each treatment variance as an estimate of the population variance. With dispersion = "cip", intervals use the mean square error (MSE).

Value

'NULL' (invisibly). The main purpose of this function is to produce a plot.

Author(s)

Faria, J. C. (joseclaudio.faria@gmail.com)
Jelihovschi, E. G. (eniojelihovs@gmail.com)
Allaman, I. B. (ivanalaman@gmail.com)

References

Murrell, P. (2005) R Graphics. Chapman and Hall/CRC Press.

See Also

plot

Examples

##
## Examples: Completely Randomized Design (CRD)
## More details: demo(package='TukeyC')
##

library(TukeyC)
data(CRD2)

## From: formula
tk1 <- with(CRD2,
            TukeyC(y ~ x,
                   data=dfm,
                   which='x'))

old.par <- par(mar=c(6, 3, 6, 2))
plot(tk1,
     id.las=2)

plot(tk1,
     yl=FALSE,
     dispersion='sd',
     id.las=2)

## From: aov
av <- with(CRD2,
           aov(y ~ x,
               data=dfm))
summary(av)

tk2 <- TukeyC(x=av,
              which='x')
plot(tk2,
     dispersion='sd',
     yl=FALSE,
     id.las=2)

# From: lm
av_lm <- with(CRD2,
           lm(y ~ x,
              data=dfm))

tk3 <- TukeyC(x=av_lm,
              which='x')

par(mfrow=c(2, 1))
plot(tk3,
     dispersion='ci',
     id.las=2,
     yl=FALSE)

plot(tk3,
     dispersion='cip',
     id.las=2,
     yl=FALSE)

par(mfrow=c(1, 1))
par(old.par)

Print Method for TukeyC objects.

Description

Returns (and prints) a list for objects of class TukeyC.

Usage

## S3 method for class 'TukeyC'
print(x, ...)

Arguments

Value

A list with the following elements:

Author(s)

Faria, J. C. (joseclaudio.faria@gmail.com)
Jelihovschi, E. G. (eniojelihovs@gmail.com)
Allaman, I. B. (ivanalaman@gmail.com)

See Also

TukeyC

Examples

data(RCBD) 

tk <- with(RCBD,
	   TukeyC(y ~ blk + tra,
		  data=dfm,
		  which='tra'))
tk

Completely Randomized Design (CRD)

Description

The experiment consists of 16 treatments (cultivars) of sorghum conducted in a balanced squared lattice design and the yield by plot (kg/plot).

Usage

  data(sorghum)

Format

An incomplete balanced block design with 4 blocks, 16 treatments, and 5 repetitions, that is, the yield of each treatment is measured 5 times. sorghum is a list with 4 elements. The first ‘⁠x⁠’ is a factor of lenght 80 with 16 levels describing the treatments. The second ‘⁠dm⁠’ is data.frame describing the design matrix. Its columns are ‘⁠x⁠’, ‘⁠bl⁠’ (blocks) and ‘⁠r⁠’ repetitions. The third ‘⁠y⁠’ is a numeric vector the yields. The fourth ‘⁠dfm⁠’ is a data frame with four columns. The first tree columns are the design matrix and the fourth is ‘⁠y⁠’.

Details

The experiment was conducted at EMBRAPA Milho e Sorgo (The Brazilian Agricultural Research Corporation, Corn and Sorghum section).

Source

Ramalho, M.A.P. and Ferreira and D.F. and Oliveira, A.C. (2000) Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA, Lavras, Brazil, page 167.

Examples

  library(TukeyC)

  data(sorghum) 

  av <- aov(y ~ r/bl + x,
            data=sorghum$dfm)

  tk <- TukeyC(av,
               which='x',
               sig.level=0.05) 

  summary(tk)

  plot(tk)

Summary Method for TukeyC and TukeyC.nest Objects

Description

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

Usage

  ## S3 method for class 'TukeyC'
summary(object,
        complete=TRUE, ...)

Arguments

Value

A matrix if complete is TRUE, or a data.frame if complete is FALSE.

Author(s)

Faria, J. C. (joseclaudio.faria@gmail.com)
Jelihovschi, E. G. (eniojelihovs@gmail.com)
Allaman, I. B. (ivanalaman@gmail.com)

References

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

See Also

TukeyC

Examples

  ##
  ## Examples: Completely Randomized Design (CRD)
  ## More details: demo(package='TukeyC')
  ##
  
  ## The parameters can be: formula, aov, lm and aovlist
  data(CRD2)
  
  ## From: formula
  tk1 <- with(CRD2,
              TukeyC(y ~ x,
                     data=dfm,
                     which='x',
                     id.trim=5))
  summary(tk1)

  ##
  ## Example: Randomized Complete Block Design (RCBD)
  ## More details: demo(package='TukeyC')
  ##
  
  ## The parameters can be: formula, aov, lm and aovlist 
  data(RCBD)
  
  ## From: formula
  tk2 <- with(RCBD,
              TukeyC(y ~ blk + tra,
                     data=dfm,
                     which='tra'))
  summary(tk2)

  ##
  ## 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: formula
  tk3 <- with(LSD,
              TukeyC(y ~ rows + cols + tra,
                     data=dfm,
                     which='tra'))
  summary(tk3)

  ##
  ## 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
  tk4 <- with(FE,
              TukeyC(y ~ blk + N*P*K,
                     data=dfm,
                     which='N'))
  summary(tk4)

  ## Nested: p1/N
  ## Testing N inside of level one of P
  ntk1 <- with(FE,
               TukeyC(y ~ blk + N*P*K,
                      data=dfm,
                      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(y ~ blk + N*P*K,
                      data=dfm,
                      which='K:P:N',
                      fl1=1,
                      fl2=1))
  summary(ntk2)

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

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

  ##
  ## Example: Split-plot Experiment (SPE)
  ## More details: demo(package='TukeyC')
  ##
  
  data(SPE)
  ## From: formula
  ## Main factor: P
  ## Specify the appropriate error term
  tk1 <- with(SPE,
              TukeyC(y ~ blk + P*SP + Error(blk/P),
                     data=dfm,
                     which='P',
                     error='blk:P'))
  summary(tk1)

  ## Nested: p1/SP
  tkn1 <- with(SPE,
               TukeyC(y ~ blk + P*SP + Error(blk/P),
                      data=dfm,
                      which='P:SP',
                      fl1=1 ))
  summary(tkn1)

  ## From: formula
  ## Main factor: P
  ## Specify the appropriate error term
  data(SSPE) 

  tk1 <- with(SSPE,
              TukeyC(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                     data=dfm,
                     which='P',
                     error='blk:P'))
  summary(tk1)

  ## Main factor: SP
  ## Specify the appropriate error term
  tk2 <- with(SSPE,
              TukeyC(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                     data=dfm,
                     which='SP',
                     error='blk:P:SP'))
  summary(tk2)

  ## Main factor: SSP
  tk3 <- with(SSPE,
              TukeyC(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                     data=dfm,
                     which='SSP'))
  summary(tk3)
  
  ## Nested: p1/SSP
  tkn1 <- with(SSPE,
               TukeyC(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                      data=dfm,
                      which='P:SSP',
                      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(av,
                 which='P:SP:SSP',
                 fl1=1,
                 fl2=1)
  summary(tkn2)

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

  ## Nested: SSP2/P1/SP - specify how to combine error terms
  tkn4 <- TukeyC(av,
                 which='SSP:P:SP',
                 fl1=2,
                 fl2=1,
                 error='Within/blk:P/blk:P:SP')
  summary(tkn4)

Create a Table for Export

Description

This function is re-exported from the xtable package so that xtable() is available after library(TukeyC) without requiring a separate library(xtable) call.

For TukeyC objects the S3 method xtable.TukeyC is dispatched automatically. For full documentation of the generic see help("xtable", package = "xtable").

See Also

xtable.TukeyC, xtable

xtable method for TukeyC objects.

Description

Convert a TukeyC object to an xtable.TukeyC object, which can then be printed as a LaTeX or HTML table. This function provides an additional method for the xtable function from the xtable package.

Usage

## S3 method for class 'TukeyC'
xtable(x, ...)

## S3 method for class 'xtable.TukeyC'
print(x, include.rownames = FALSE, ...)

Arguments

Author(s)

Faria, J. C. (joseclaudio.faria@gmail.com)
Jelihovschi, E. G. (eniojelihovs@gmail.com)
Allaman, I. B. (ivanalaman@gmail.com)

See Also

xtable

Examples

data(RCBD)

lm1 <- with(RCBD,
            lm(y ~ blk + tra,
               data = dfm))

tk1 <- TukeyC(lm1,
              which = 'tra')

tb <- xtable(tk1)
## Not run: 
  print(tb)

## End(Not run)