TukeyC: The TukeyC Test for Single Experiments

In jcfaria/TukeyC: Conventional Tukey Test

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

x,formula	A formula, lm, aov, aovlist and lmerMod class object. Objects of the formula class follow “response variable ~ predicted variable.
data	A object of the data.frame class. Use only objects of formula class.
which	The name of the treatment to be used in the comparison. The name must be inside quoting marks.
fl1	A vector of length 1 giving the level of the first factor in nesting order tested.
fl2	A vector of length 1 giving the level of the second factor in nesting order tested.
error	The error to be considered. If from experiment at split plot or split-split plot pay attention! See details!
sig.level	Level of Significance used in the TukeyC algorithm to create the groups of means. The default value is 0.05.
round	Integer indicating the number of decimal places.
adjusted.pvalue	Method for adjusting p values (see p.adjust to more details). The possible values are: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr" and "none". The default is "none".
...	Potential further arguments (required by generic).

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:

Result	A data.frame storing the result of Tukey test.
Sig.level	A scalar giving the level of significance of the test.
Diff_Prob	A matrix at the lower diagonal with p-values and upper diagonal with means differences.
MSD	A matrix with minimum significance differences by Tukey methodology. If balanced data, then all values are equal.

Author(s)

Jose Claudio Faria ([e-mail])
Enio Jelihovschi ([e-mail])
Ivan Bezerra Allaman ([e-mail])

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.
## Not run: 
  if(require(lme4)){
    lmer1 <- with(RCBD,
                  lmer(y ~ (1|blk) + tra,
                       data=dfm))

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

## End(Not run)
##
## 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
## Not run: 
  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)
  }

## End(Not run)

##
## 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
## Not run: 
  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)
  }

## End(Not run)

## 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) 

jcfaria/TukeyC documentation built on July 6, 2023, 4:27 p.m.