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  pdftitle  = {Introduction to the TukeyC Package},
  pdfauthor = {Faria, J. C.; Jelihovschi, E. G.; Allaman, I. B.}
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\begin{document}

\begin{titlepage}
  \centering
  \vspace*{1.2cm}

  {\color{rulegray}\HRule}
  \vspace{0.55cm}

  {\Huge\bfseries\color{accent}%
    Introduction to the\\[0.3em]
    \texttt{TukeyC} Package}

  \vspace{0.35cm}
  {\large\color{titlemuted}%
    Multiple Comparisons Using the Tukey HSD Algorithm}

  \vspace{0.55cm}
  {\color{rulegray}\HRule}

  \vspace{2.4cm}

  \begin{minipage}[t]{0.48\textwidth}
    \raggedright
    \textbf{\color{titlemuted}Authors}\\[0.35em]
    J.~C.\ \textsc{Faria}\\
    E.~G.\ \textsc{Jelihovschi}\\
    I.~B.\ \textsc{Allaman}
  \end{minipage}%
  \hfill
  \begin{minipage}[t]{0.48\textwidth}
    \raggedleft
    \textbf{\color{titlemuted}Institution}\\[0.35em]
    Universidade Estadual\\
    de Santa Cruz --- UESC\\
    Ilh\'{e}us, Bahia, Brasil
  \end{minipage}

  \vfill

  \titleaccentline
  \vspace{0.6cm}
  {\large\today}
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)
@

\vspace{1.25cm}
\tableofcontents
\newpage

% ============================================================
\section*{Overview}
\addcontentsline{toc}{section}{Overview}

The \textbf{TukeyC} package implements Tukey's Honestly Significant Difference
(HSD) test as a multiple comparison method in the context of Analysis of
Variance (ANOVA). The package follows the conventional approach widely used in
experimental statistics: treatment means are compared using the Studentized
range, then labelled with letters that may overlap when means are not
significantly different.

The result is an easy-to-read table and plot showing mean estimates, minimum
significant differences (MSD), and optional matrices of pairwise differences
and adjusted p-values.

<<library>>=
library(TukeyC)
@

% ============================================================
\section{Quick Start --- Completely Randomized Design (CRD)}

\texttt{CRD1} contains simulated data for a balanced CRD with \textbf{4
treatment levels} and \textbf{6 replicates} per treatment. The main function
\texttt{TukeyC()} accepts a model formula, an \texttt{aov} object, or an
\texttt{lm} object. The \texttt{which} argument names the factor to be
compared.

<<crd-quick>>=
data(CRD1)

tk1 <- with(CRD1,
            TukeyC(y ~ x,
               data = dfm,
               which = 'x'))
summary(tk1)
@

The summary shows, for each level, the mean and the group letter assigned by the
algorithm. Levels sharing the same letter do not differ significantly at the
default 5\,\% level.

A single call to \texttt{plot()} produces the canonical dot plot with group
letters displayed above each point:

<<crd-plot, fig.cap="CRD1: treatment means with min-max dispersion bars and TukeyC groups.">>=
plot(tk1,
     dispersion = 'mm',
     d.col = 'steelblue')
@

% ============================================================
\section{Accepted Input Classes}

\texttt{TukeyC()} dispatches on the class of its first argument. The same grouping
can be obtained from a \texttt{formula}, an \texttt{aov} object, or an
\texttt{lm} object.

<<input-classes>>=
## From: aov
av1 <- with(CRD1, aov(y ~ x, data = dfm))
tk2 <- TukeyC(av1, which = 'x')
summary(tk2)

## From: lm
lm1 <- with(CRD1, lm(y ~ x, data = dfm))
tk3 <- TukeyC(lm1, which = 'x')
summary(tk3)
@

% ============================================================
\section{Unbalanced Data}

When observations are missing, \texttt{TukeyC()} automatically adjusts the means
using the Least-Squares Means methodology (via the \textbf{emmeans} package).
The analysis proceeds identically to the balanced case.

<<crd-unbalanced>>=
## Remove the first observation to create an unbalanced dataset
u_tk1 <- with(CRD1,
              TukeyC(y ~ x,
                 data = dfm[-1, ],
                 which = 'x'))
summary(u_tk1)
@

The number of replicates shown at the bottom of the plot reflects the actual
(unequal) sample sizes:

<<plot-unbal, fig.cap="CRD1 (unbalanced): adjusted means with SD bars.">>=
plot(u_tk1, dispersion = 'sd', d.col = 'tomato')
@

% ============================================================
\section{Randomized Complete Block Design (RCBD)}

\texttt{RCBD} contains simulated data for a design with \textbf{5 treatment
levels} and \textbf{4 blocks}. The blocking factor \texttt{blk} is included in
the formula; \texttt{which} selects the factor of interest for the comparison.

<<rcbd>>=
data(RCBD)

tk4 <- with(RCBD,
            TukeyC(y ~ blk + tra,
               data = dfm,
               which = 'tra'))
summary(tk4)
@

<<rcbd-plot, fig.cap="RCBD: treatment means with individual CI bars.">>=
plot(tk4,
     dispersion = 'ci',
     d.col = 'darkgreen',
     d.lty = 2)
@

% ============================================================
\section{Significance Level}

The default significance level is \texttt{sig.level = 0.05}. Stricter or looser
levels lead to fewer or more groups, respectively.

<<sig-level>>=
## alpha = 0.01 (stricter)
tk_01 <- with(RCBD,
              TukeyC(y ~ blk + tra,
                 data = dfm,
                 which = 'tra',
                 sig.level = 0.01))

## alpha = 0.10 (looser)
tk_10 <- with(RCBD,
              TukeyC(y ~ blk + tra,
                 data = dfm,
                 which = 'tra',
                 sig.level = 0.10))

cat('--- sig.level = 0.01 ---\n')
summary(tk_01)

cat('--- sig.level = 0.10 ---\n')
summary(tk_10)
@

% ============================================================
\section{Factorial Experiment (FE)}

\texttt{FE} contains simulated data for a \textbf{3-factor factorial} design
(N, P, K), each at 2 levels, in 4 blocks. \texttt{TukeyC()} supports both
main-effect and nested comparisons using colon notation in \texttt{which} and
the \texttt{fl1} / \texttt{fl2} arguments to select the level of the nesting
factor.

<<fe-main>>=
data(FE)

## Main effect: factor N
tk5 <- with(FE,
            TukeyC(y ~ blk + N*P*K,
               data = dfm,
               which = 'N'))
summary(tk5)
@

<<fe-nested>>=
## Nested: levels of N within level 1 of P
tk6 <- with(FE,
            TukeyC(y ~ blk + N*P*K,
               data = dfm,
               which = 'P:N',
               fl1 = 1))
summary(tk6)

## Nested: levels of N within level 2 of P
tk7 <- with(FE,
            TukeyC(y ~ blk + N*P*K,
               data = dfm,
               which = 'P:N',
               fl1 = 2))
summary(tk7)
@

% ============================================================
\section{Split-Plot Experiment (SPE)}

\texttt{SPE} contains simulated data for a design with \textbf{3 whole plots}
(factor P) and \textbf{4 sub-plot treatments} (factor SP). When testing the
whole-plot factor, the appropriate error term must be specified via the
\texttt{error} argument.

<<spe>>=
data(SPE)

## Sub-plot factor SP (residual error, default)
tk8 <- with(SPE,
            TukeyC(y ~ blk + P*SP + Error(blk/P),
               data = dfm,
               which = 'SP'))
summary(tk8)

## Whole-plot factor P (must specify the blk:P error term)
tk9 <- with(SPE,
            TukeyC(y ~ blk + P*SP + Error(blk/P),
               data = dfm,
               which = 'P',
               error = 'blk:P'))
summary(tk9)
@

% ============================================================
\section{Visualisation Options}

\subsection{Dispersion bars}

Four dispersion options are available for \texttt{plot.TukeyC()}, as summarised in
Table~\ref{tab:disp}.

\begin{table}[H]
\centering
\caption{Dispersion options available for \texttt{plot.TukeyC()}.}
\label{tab:disp}
\begin{tabular}{ll}
\toprule
Option           & Description \\
\midrule
\texttt{'mm'}  & Min-max range (default) \\
\texttt{'sd'}  & $\pm 1$ standard deviation \\
\texttt{'ci'}  & Individual 95\,\% confidence interval \\
\texttt{'cip'} & Pooled 95\,\% confidence interval (uses MSE) \\
\bottomrule
\end{tabular}
\end{table}

\texttt{CRD2} provides a more visually rich example with \textbf{45 treatment
levels}:

<<plot-crd2, fig.width=8, fig.height=5, fig.cap="CRD2: 45 treatment means with pooled CI bars.">>=
data(CRD2)

tk10 <- with(CRD2,
             TukeyC(y ~ x,
                data = dfm,
                which = 'x'))

col=c(rep(2, 6),
      rep(3, 36),
      rep(4, 1),
      rep(5, 2))

plot(tk10,
     dispersion='cip',
     yl=FALSE,
     id.las=2,
     col=col,
     d.col=col)
@

\subsection{Comparing all four options}

<<plot-four, fig.width=8, fig.height=7, fig.cap="The four dispersion options applied to CRD1. (A) mm: min-max range; (B) sd: standard deviation; (C) ci: individual confidence interval; (D) cip: pooled confidence interval.">>=
op <- par(mfrow = c(2, 2), mar = c(4, 3, 4, 1))

plot(tk1, dispersion = 'mm',  d.col = 'steelblue')
mtext('(A)', side = 3, adj = 0, line = 2, font = 2)

plot(tk1, dispersion = 'sd',  d.col = 'tomato')
mtext('(B)', side = 3, adj = 0, line = 2, font = 2)

plot(tk1, dispersion = 'ci',  d.col = 'darkgreen')
mtext('(C)', side = 3, adj = 0, line = 2, font = 2)

plot(tk1, dispersion = 'cip', d.col = 'purple')
mtext('(D)', side = 3, adj = 0, line = 2, font = 2)

par(op)
@

\subsection{Boxplot}

\texttt{boxplot.TukeyC()} extends the standard boxplot by overlaying the TukeyC group
letters above the frame and drawing the treatment mean inside each box.

<<boxplot, fig.cap="CRD1: boxplot with TukeyC group labels and means (red line).">>=
## boxplot.TukeyC re-evaluates the data argument from the original call;
## pass CRD1$dfm directly so it is findable in any environment.
tk1_bp <- TukeyC(y ~ x,
             data = CRD1$dfm,
             which = 'x')

boxplot(tk1_bp,
        mean.col = 'red',
        mean.lwd = 2,
        args.legend = list(x = 'topright'))
@

% ============================================================
\section{Tabular Output}

\texttt{xtable()} converts a \texttt{TukeyC} result to an \texttt{xtable} object
for inclusion in \LaTeX{} or HTML documents. Table~\ref{tab:rcbd} shows the
Tukey HSD grouping for the RCBD example.

<<xtable, results='asis'>>=
library(xtable)

tb <- xtable(tk4,
             caption = 'RCBD: Tukey HSD grouping of treatment means.',
             label = 'tab:rcbd',
             digits = 3)
print(tb,
      type = 'latex',
      caption.placement = 'top',
      include.rownames = FALSE,
      booktabs = TRUE,
      table.placement = 'H')
@

% ============================================================
\section{Mixed Models with lme4}

\texttt{TukeyC()} also accepts \texttt{lmerMod} objects from the \textbf{lme4}
package, useful when random effects need to be modelled explicitly.

<<lmer, eval=requireNamespace('lme4', quietly=TRUE)>>=
library(lme4)

data(RCBD)

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

tk11 <- TukeyC(lmer1, which = 'tra')
summary(tk11)
@

% ============================================================
\section*{References}
\addcontentsline{toc}{section}{References}

\begin{thebibliography}{9}

\bibitem{tukey1949}
Tukey, J. W. (1949).
Comparing individual means in the analysis of variance.
\textit{Biometrics}, \textbf{5}(2), 99--114.
\href{https://doi.org/10.2307/3001913}{doi:10.2307/3001913}

\bibitem{miller1981}
Miller, R. G. (1981).
\textit{Simultaneous Statistical Inference}.
Springer.

\end{thebibliography}

\end{document}
