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 (‘⁠SPE⁠’).

The package can be used for both balanced or unbalanced (when possible), experiments.

R has some functions (TukeyHSD provided by stats, glht provided by multcomp, HSD.test provided by agricolae and cld provided by multcomp) which also performs 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: the p-value is showed allowing the user to flexibilize the inferencial decision and also 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, most of users of other statistical softwares are very used with letters grouping the means of the factor tested, making unattractive or difficult to adapt to the current aproach of R.

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

Author(s)

Jose Claudio Faria (joseclaudio.faria@gmail.com)
Enio Jelihovschi (eniojelihovs@gmail.com)
Ivan Bezerra Allaman (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 listillustrating 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 factor 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 factor levels repetitions one in each block.

Split-plot Experiment (SPE)

Description

A list to ilustrate 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 ilustrate 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 whenever the user want a specific error other than the experimental error. At the split plot and split-split plot experiment, combination of error may be specified with "/" in the sequence of the which argument. For example, a object of aovlist class, a possible combination would be error = 'Within/blk:plot' at case block split plot experiment with which = 'subplot:plot' argument.

Value

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

Author(s)

Jose Claudio Faria (joseclaudio.faria@gmail.com)
Enio Jelihovschi (eniojelihovs@gmail.com)
Ivan Bezerra Allaman (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: merMod
## 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: merMod

  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))

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

# year1/tra
# It is necessary to set year error with trat error in the order of the "which" argument.
# It is necessary to inform how to combinate the errors
sp_tk2 <-  TukeyC(lm3,
                  which='year:tra',
                  error='Residuals/blk:tra',
                  fl1=1)
summary(sp_tk2)

# From merMod
# Onty 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
## It is necessary to inform the appropriate error for the test
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
## It is necessary to inform the appropriate error for the test
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
## It is necessary to inform the appropriate error for the test
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
## It is necessary to inform the appropriate error for the test
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
## It is necessary to inform the appropriate error for the test
ssp_tk11 <- TukeyC(av3,
                   which='SSP:SP:P',
                   error='Within/blk:P:SP/blk:P',
                   fl1=1,
                   fl2=1)
summary(ssp_tk11)

## UNBALANCED DATA
## The average are adjusted by "Least-Square-Means" methodology.
## 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 a S3 method to plot ‘⁠TukeyC⁠’ objetcs. The difference to generic function is the Tukey inference under frame and the plot of means within box.

Value

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

Author(s)

Jose Claudio Faria (joseclaudio.faria@gmail.com)
Enio Jelihovschi (eniojelihovs@gmail.com)
Ivan Bezerra Allaman (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 the experiment variation

Description

It obtains the coefficient of variation of the experiment obtained by models lm, aov and aovlist.

Usage

cv(x, 
   round=2)

Arguments

Details

sqrt(MSerror)*100/mean(x)

Value

Author(s)

Jose Claudio Faria (joseclaudio.faria@gmail.com)
Enio Jelihovschi (eniojelihovs@gmail.com)
Ivan Bezerra Allaman (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 groups of means, according to the method of Tukey.

Usage

  make.TukeyC.groups(x)

Arguments

Details

This function is an algorithm used to build up the groups of means, allowing for overlapping.

Value

The object returned by this function is a character matrix and the rownames are the levels of the factor being tested sorted in decreasing order. The matrix has the same number of columns as the number of groups generated.

Note

This function is mainly for internal use in the TukeyC package.

Author(s)

Jose Claudio Faria (joseclaudio.faria@gmail.com)
Enio Jelihovschi (eniojelihovs@gmail.com)
Ivan Bezerra Allaman (ivanalaman@gmail.com)

Make Tukey Test

Description

This function implements the Tukey test for balanced or unbalanced designs and schemes.

Usage

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

Arguments

Value

A list with 5 slots containing the most important results of the test performed:

Note

This function is mainly for internal use in the TukeyC package.

Author(s)

Jose Claudio Faria (joseclaudio.faria@gmail.com)
Enio Jelihovschi (eniojelihovs@gmail.com)
Ivan Bezerra Allaman (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('none','mm','sd','ci','cip'),
     d.lty          = 1,
     d.col          = 'black',
     title          = '', ...)

Arguments

Details

The plot.TukeyC function is a S3 method to plot ‘⁠Tukey⁠’ and TukeyC.nest objetcs. It generates a serie of points (the means) and a vertical line showing the dispersion of the values corresponding to each group mean. The ci options is calculed utilizing each treatment variance as estimating of population variance. The cip options is calculed utilizing the means square error (MSE) as estimating of population variance.

Value

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

Author(s)

Jose Claudio Faria (joseclaudio.faria@gmail.com)
Enio Jelihovschi (eniojelihovs@gmail.com)
Ivan Bezerra Allaman (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,
     disp='sd',
     id.las=2)

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

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

plot(tk3,
     disp='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)

Jose Claudio Faria (joseclaudio.faria@gmail.com)
Enio G. Jelihovschi (eniojelihovs@gmail.com)
Ivan Bezerra Allaman (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 argument iqual TRUE or data.frame if complete argument iqual FALSE.

Author(s)

Jose Claudio Faria (joseclaudio.faria@gmail.com)
Enio Jelihovschi (eniojelihovs@gmail.com)
Ivan Bezerra Allaman (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
  ## It is necessary to inform the appropriate error for the test
  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
  ## It is necessary to inform the appropriate error for the test
  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
  ## It is necessary to inform the appropriate error for the test
  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 - it is necessary to inform how to combinate the errors
  tkn4 <- TukeyC(av,
                 which='SSP:P:SP',
                 fl1=2,
                 fl2=1,
                 error='Within/blk:P/blk:P:SP')
  summary(tkn4)

xtable method for TukeyC objects.

Description

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

Usage

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

Arguments

Value

For most xtable methods, an object of class "xtable.TukeyC", "xtable" which inherits the data.frame class and contains several additional attributes specifying the table formatting options.

Author(s)

Jose Claudio Faria (joseclaudio.faria@gmail.com)
Enio G. Jelihovschi (eniojelihovs@gmail.com)
Ivan Bezerra Allaman (ivanalaman@gmail.com)

See Also

xtable

Examples

data(RCBD) 

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

    tk1 <-  TukeyC(lm1,
                   which='tra')
    tb <- xtable(tk1)
    print(tb)
  }