| Title: | Conventional Tukey Test |
|---|---|
| Description: | Perform the conventional Tukey test from formula, lm, aov, aovlist and lmer objects. |
| Authors: | Jose Claudio Faria <[email protected]>, Enio G. Jelihovschi <[email protected]>, Ivan Bezerra Allaman <[email protected]> |
| Maintainer: | Ivan Bezerra Allaman <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.3-41 |
| Built: | 2024-11-15 03:05:43 UTC |
| Source: | https://github.com/jcfaria/tukeyc |
S3 method to plot TukeyC objects.
## 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, ...)## 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, ...)
x |
A |
mean.type |
The type of mean must be ploted. O default is "line". |
xlab |
A label for the ‘x’ axis. |
mean.col |
A vector of colors for the means representation. |
mean.pch |
A vector of plotting symbols or characters. Only if type are "point". |
mean.lwd |
Line width of mean. |
mean.lty |
Line type of mean. Only if type are "line". |
args.legend |
List of additional arguments to be passed to |
... |
Optional plotting parameters. |
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.
Jose Claudio Faria ([email protected])
Enio Jelihovschi ([email protected])
Ivan Bezerra Allaman ([email protected])
Murrell, P. (2005) R Graphics. Chapman and Hall/CRC Press.
## ## Examples: Completely Randomized Design (CRD) ## More details: demo(package='TukeyC') ## library(TukeyC) data(CRD1) ## From: formula # Simple! tk1 <- TukeyC(y ~ x, data=CRD1$dfm, which='x') boxplot(tk1) # A little more elaborate! boxplot(tk1, mean.lwd=1.3, mean.col='red') # A little more! boxplot(tk1, mean.lwd=1.3, mean.lty=2, mean.col='red', args.legend=list(x='bottomleft')) # With point type! boxplot(tk1, mean.type='point') boxplot(tk1, mean.type='point', mean.pch=19, cex=1.5, mean.col='red') # With other point boxplot(tk1, mean.type='point', mean.pch='+', cex=2, mean.col='blue', args.legend=list(x='bottomleft'))## ## Examples: Completely Randomized Design (CRD) ## More details: demo(package='TukeyC') ## library(TukeyC) data(CRD1) ## From: formula # Simple! tk1 <- TukeyC(y ~ x, data=CRD1$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'))
A list illustrating the resources of TukeyC package
related to Completely Randomized Design (‘CRD’).
data(CRD1)data(CRD1)
A simulated data to model a Completely Randomized Design (‘CRD’) of 4 factor levels and 6 repetitions.
A listillustrating the resources of TukeyC package
related to Completely Randomized Design (‘CRD’).
data(CRD2)data(CRD2)
A simulated data to model a Completely Randomized Design (‘CRD’) of 45 factor levels and 4 repetitions.
It obtains the coefficient of variation of the experiment obtained by
models lm, aov and aovlist.
cv(x, round=2)cv(x, round=2)
x |
A object of the class |
round |
An integer value indicating the number of decimal places to be used. The default value is 2. |
sqrt(MSerror)*100/mean(x)
x |
named numeric vector |
Jose Claudio Faria ([email protected])
Enio Jelihovschi ([email protected])
Ivan Bezerra Allaman ([email protected])
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]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]
A list illustrating the resources of TukeyC package
related to Factorial Experiment (‘FE’).
data(FE)data(FE)
A simulated data to model a Factorial Experiment (‘FE’) with 3 factors, 2 levels per factor and 4 blocks.
A list illustrating the resources of TukeyC package
related to Latin Squares Design (‘LSD’).
data(LSD)data(LSD)
A simulated data to model a Latin Squares Design (‘LSD’) with 5 factor levels 5 rows and 5 columns.
Builds groups of means, according to the method of Tukey.
make.TukeyC.groups(x)make.TukeyC.groups(x)
x |
A square matrix where the lines and colums are all levels of the
factor being test sorted in decresing order. The matrix content are
|
This function is an algorithm used to build up the groups of means, allowing for overlapping.
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.
This function is mainly for internal use in the TukeyC package.
Jose Claudio Faria ([email protected])
Enio Jelihovschi ([email protected])
Ivan Bezerra Allaman ([email protected])
This function implements the Tukey test for balanced or unbalanced designs and schemes.
make.TukeyC.test(obj, MSE, sig.level, dfr, round, adjusted.pvalue)make.TukeyC.test(obj, MSE, sig.level, dfr, round, adjusted.pvalue)
obj |
A data.frame with the means and replicate of the factors. |
MSE |
A vector of length 1 giving the mean squared error. |
sig.level |
A vector of length 1 giving the level of significance of the test. |
dfr |
A vector of length 1 giving the degrees of freedom of ‘MSE’. |
round |
Integer indicating the number of decimal places. |
adjusted.pvalue |
A vector of pvalues obtained of the ptukey function. |
A list with 5 slots containing the most important results of the test performed:
Result |
The result of the Tukey test |
Sig.Level |
The significance of the test |
Diff_Prob |
A |
MSD |
The minimum significative difference |
Replicates |
The replicate number for each factor |
This function is mainly for internal use in the TukeyC package.
Jose Claudio Faria ([email protected])
Enio Jelihovschi ([email protected])
Ivan Bezerra Allaman ([email protected])
S3 method to plot TukeyC and TukeyC.nest objects.
## 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 = '', ...)## 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 = '', ...)
x |
A |
result |
The result of the test (letters) should be visible. |
replicates |
The number of replicates should be visible. |
pch |
A vector of plotting symbols or characters. |
col |
A vector of colors for the means representation. |
xlab |
A label for the ‘x’ axis. |
ylab |
A label for the ‘y’ axis. |
xlim |
The ‘x’ limits of the plot. |
ylim |
The ‘y’ limits of the plot. |
axisx |
Axis x? If ‘TRUE’ you must accept the default, otherwise, you must customize. |
axisy |
Axis y? If ‘TRUE’ you must accept the default, otherwise, you must customize. |
id.lab |
Factor level names at ‘x’ axis. |
id.las |
Factor level names written either horizontally or vertically. |
yl |
Horizontal (reference) line connecting the circle to the ‘y’ axis. |
yl.lty |
Line type of ‘yl’. |
yl.col |
Line color of ‘yl’. |
dispersion |
Vertical line through the circle (mean value) linking the minimum to the maximum of the factor level values corresponding to that mean value. Other options are: sd (standard deviation), ci (confidence interval), cip (pooled confidence interval) and none. |
d.lty |
Line type of dispersion. |
d.col |
A vector of colors for the line type of dispersion. |
title |
A title for the plot. |
... |
Optional plotting parameters. |
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.
Jose Claudio Faria ([email protected])
Enio Jelihovschi ([email protected])
Ivan Bezerra Allaman ([email protected])
Murrell, P. (2005) R Graphics. Chapman and Hall/CRC Press.
## ## 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)## ## 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)
TukeyC objects.
Returns (and prints) a list for objects of class TukeyC.
## S3 method for class 'TukeyC' print(x, ...)## S3 method for class 'TukeyC' print(x, ...)
x |
A given object of the class |
... |
Further arguments (require by generic). |
Jose Claudio Faria ([email protected])
Enio G. Jelihovschi ([email protected])
Ivan Bezerra Allaman ([email protected])
data(RCBD) tk <- with(RCBD, TukeyC(y ~ blk + tra, data=dfm, which='tra')) tkdata(RCBD) tk <- with(RCBD, TukeyC(y ~ blk + tra, data=dfm, which='tra')) tk
A list illustrating the resources of TukeyC package
related to Randomized Complete Block Design (‘RCBD’).
data(RCBD)data(RCBD)
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.
The experiment consists of 16 treatments (cultivars) of sorghum conducted in a balanced squared lattice design and the yield by plot (kg/plot).
data(sorghum)data(sorghum)
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’.
The experiment was conducted at EMBRAPA Milho e Sorgo (The Brazilian Agricultural Research Corporation, Corn and Sorghum section).
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.
library(TukeyC) data(sorghum) av <- aov(y ~ r/bl + x, data=sorghum$dfm) tk <- TukeyC(av, which='x', sig.level=0.05) summary(tk) plot(tk)library(TukeyC) data(sorghum) av <- aov(y ~ r/bl + x, data=sorghum$dfm) tk <- TukeyC(av, which='x', sig.level=0.05) summary(tk) plot(tk)
A list to ilustrate the resources of TukeyC package
related to Split-plot Experiment (‘SPE’).
data(SPE)data(SPE)
A simulated data to model a Split-plot Experiment (‘SPE’) with 3 plots, each one split 4 times and 6 repetitions per split.
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).
data(SPET)data(SPET)
Gomes, F.P. (1990). Curso de Estatistica Experimental. 13 ed. Editora NOBEL, Piracicaba, Brazil, page 157.
A list to ilustrate the resources of TukeyC package
related to Split-split-plot Experiment (‘SSPE’).
data(SSPE)data(SSPE)
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.
Returns (and prints) a summary list for TukeyC objects.
## S3 method for class 'TukeyC' summary(object, complete=TRUE, ...)## S3 method for class 'TukeyC' summary(object, complete=TRUE, ...)
object |
A given object of the class |
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). |
Jose Claudio Faria ([email protected])
Enio Jelihovschi ([email protected])
Ivan Bezerra Allaman ([email protected])
Chambers, J.M. and Hastie, T.J. (1992) Statistical Models in S. Wadsworth and Brooks/Cole.
## ## 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)## ## 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)
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.
Jose Claudio Faria ([email protected])
Enio Jelihovschi ([email protected])
Ivan Bezerra Allaman ([email protected])
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.
These are methods for objects of class formula, lm, aov, aovlist and lmerMod for single, factorial, split-plot and split-split-plot experiments.
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', ...)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', ...)
x, formula
|
A |
data |
A object of the |
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 |
... |
Potential further arguments (required by generic). |
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.
The function TukeyC returns a list of the class TukeyC with the slots:
Result |
A |
Sig.level |
A scalar giving the level of significance of the test. |
Diff_Prob |
A |
MSD |
A |
Jose Claudio Faria ([email protected])
Enio Jelihovschi ([email protected])
Ivan Bezerra Allaman ([email protected])
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: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)## ## 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)
TukeyC objects.
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.
## S3 method for class 'TukeyC' xtable(x, ...)## S3 method for class 'TukeyC' xtable(x, ...)
x |
A given object of the class |
... |
Further arguments (require by xtable::xtable). |
Jose Claudio Faria ([email protected])
Enio G. Jelihovschi ([email protected])
Ivan Bezerra Allaman ([email protected])
data(RCBD) ## Not run: if(require(xtable)){ lm1 <- with(RCBD, lm(y ~ blk + tra, data=dfm)) tk1 <- TukeyC(lm1, which='tra') tb <- xtable(tk1) print(tb) } ## End(Not run)data(RCBD) ## Not run: if(require(xtable)){ lm1 <- with(RCBD, lm(y ~ blk + tra, data=dfm)) tk1 <- TukeyC(lm1, which='tra') tb <- xtable(tk1) print(tb) } ## End(Not run)