## Copyright (C) 2003-2005 Andy Adler <adler@ncf.ca>
## Copyright (C) 2021 Christian Scholz
## Copyright (C) 2022 Andreas Bertsatos <abertsatos@biol.uoa.gr>
## Copyright (C) 2022 Andrew Penn <A.C.Penn@sussex.ac.uk>
## Copyright (C) 2024 Swayam Shah <swayamshah66@gmail.com>
##
## This file is part of the statistics package for GNU Octave.
##
## This program is free software; you can redistribute it and/or modify it under
## the terms of the GNU General Public License as published by the Free Software
## Foundation; either version 3 of the License, or (at your option) any later
## version.
##
## This program is distributed in the hope that it will be useful, but WITHOUT
## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
## details.
##
## You should have received a copy of the GNU General Public License along with
## this program; if not, see <http://www.gnu.org/licenses/>.

## -*- texinfo -*-
## @deftypefn  {statistics} {@var{p} =} anovan (@var{Y}, @var{GROUP})
## @deftypefnx {statistics} {@var{p} =} anovan (@var{Y}, @var{GROUP}, @var{name}, @var{value})
## @deftypefnx {statistics} {[@var{p}, @var{atab}] =} anovan (@dots{})
## @deftypefnx {statistics} {[@var{p}, @var{atab}, @var{stats}] =} anovan (@dots{})
## @deftypefnx {statistics} {[@var{p}, @var{atab}, @var{stats}, @var{terms}] =} anovan (@dots{})
##
## Perform a multi (N)-way analysis of (co)variance (ANOVA or ANCOVA) to
## evaluate the effect of one or more categorical or continuous predictors (i.e.
## independent variables) on a continuous outcome (i.e. dependent variable). The
## algorithms used make @code{anovan} suitable for balanced or unbalanced
## factorial (crossed) designs. By default, @code{anovan} treats all factors
## as fixed. Examples of function usage can be found by entering the command
## @code{demo anovan}. A bootstrap resampling variant of this function,
## @code{bootlm}, is available in the statistics-resampling package and has
## similar usage.
##
## Data is a single vector @var{Y} with groups specified by a corresponding
## matrix or cell array of group labels @var{GROUP}, where each column of
## @var{GROUP} has the same number of rows as @var{Y}. For example, if
## @code{@var{Y} = [23; 27; 31; 29; 30; 32]; @var{GROUP} = [1, 2; 1, 3; 1, 2; 2, 3; 2, 3; 3, 2];}
## then observation 23 was measured under conditions 1,2; observation 27 was
## measured under conditions 1,3; and so on. If the @var{GROUP} provided is empty,
## then the linear model is fit with just the intercept (no predictors).
##
## @code{anovan} can take a number of optional parameters as name-value pairs.
##
## @code{[@dots{}] = anovan (@var{Y}, @var{GROUP}, "continuous", @var{continuous})}
##
## @itemize
## @item
## @var{continuous} is a vector of indices indicating which of the columns (i.e.
## factors) in @var{GROUP} should be treated as continuous predictors rather
## than as categorical predictors. The relationship between continuous
## predictors and the outcome should be linear.
## @end itemize
##
## @code{[@dots{}] = anovan (@var{Y}, @var{GROUP}, "random", @var{random})}
##
## @itemize
## @item
## @var{random} is a vector of indices indicating which of the columns (i.e.
## factors) in @var{GROUP} should be treated as random effects rather than
## fixed effects. Octave @code{anovan} provides only basic support for random
## effects. Specifically, since all F-statistics in @code{anovan} are
## calculated using the mean-squared error (MSE), any interaction terms
## containing a random effect are dropped from the model term definitions and
## their associated variance is pooled with the residual, unexplained variance
## making up the MSE. In effect, the model then fitted equates to a linear mixed
## model with random intercept(s). Variable names for random factors are
## appended with a ' symbol.
## @end itemize
##
## @code{[@dots{}] = anovan (@var{Y}, @var{GROUP}, "model", @var{modeltype})}
##
## @itemize
## @item
## @var{modeltype} can specified as one of the following:
##
## @itemize
## @item
## "linear" (default) : compute @math{N} main effects with no interactions.
##
## @item
## "interaction" : compute @math{N} effects and @math{N*(N-1)} two-factor
## interactions
##
## @item
## "full" : compute the @math{N} main effects and interactions at all levels
##
## @item
## a scalar integer : representing the maximum interaction order
##
## @item
## a matrix of term definitions : each row is a term and each column is a factor
## @end itemize
##
## @example
## -- Example:
## A two-way ANOVA with interaction would be: [1 0; 0 1; 1 1]
## @end example
##
## @end itemize
##
## @code{[@dots{}] = anovan (@var{Y}, @var{GROUP}, "sstype", @var{sstype})}
##
## @itemize
## @item
## @var{sstype} can specified as one of the following:
##
## @itemize
## @item
## 1 : Type I sequential sums-of-squares.
##
## @item
## 2 or "h" : Type II partially sequential (or hierarchical) sums-of-squares
##
## @item
## 3 (default) : Type III partial, constrained or marginal sums-of-squares
##
## @end itemize
## @end itemize
##
## @code{[@dots{}] = anovan (@var{Y}, @var{GROUP}, "varnames", @var{varnames})}
##
## @itemize
## @item
## @var{varnames} must be a cell array of strings with each element containing a
## factor name for each column of @var{GROUP}.  By default (if not parsed as
## optional argument), @var{varnames} are "X1","X2","X3", etc.
## @end itemize
##
## @code{[@dots{}] = anovan (@var{Y}, @var{GROUP}, "alpha", @var{alpha})}
##
## @itemize
## @item
## @var{alpha} must be a scalar value between 0 and 1 requesting
## @math{100*(1-@var{alpha})%} confidence bounds for the regression coefficients
## returned in @var{stats}.coeffs (default 0.05 for 95% confidence).
## @end itemize
##
## @code{[@dots{}] = anovan (@var{Y}, @var{GROUP}, "display", @var{dispopt})}
##
## @itemize
## @item
## @var{dispopt} can be either "on" (default) or "off" and controls the display
## of the model formula, table of model parameters, the ANOVA table and the
## diagnostic plots. The F-statistic and p-values are formatted in APA-style.
## To avoid p-hacking, the table of model parameters is only displayed if we set
## planned contrasts (see below).
## @end itemize
##
## @code{[@dots{}] = anovan (@var{Y}, @var{GROUP}, "contrasts", @var{contrasts})}
##
## @itemize
## @item
## @var{contrasts} can be specified as one of the following:
##
## @itemize
## @item
## A string corresponding to one of the built-in contrasts listed below:
##
## @itemize
## @item
## "simple" or "anova" (default): Simple (ANOVA) contrast coding. (The first
## level appearing in the @var{GROUP} column is the reference level)
##
## @item
## "poly": Polynomial contrast coding for trend analysis.
##
## @item
## "helmert": Helmert contrast coding: the difference between each level with
## the mean of the subsequent levels.
##
## @item
## "effect": Deviation effect coding. (The first level appearing in the
## @var{GROUP} column is omitted).
##
## @item
## "sdif" or "sdiff": Successive differences contrast coding: the difference
## between each level with the previous level.
##
## @item
## "treatment": Treatment contrast (or dummy) coding. (The first level appearing
## in the @var{GROUP} column is the reference level). These contrasts are not
## compatible with @var{sstype} = 3.
##
## @end itemize
##
## @item
## A matrix containing a custom contrast coding scheme (i.e. the generalized
## inverse of contrast weights). Rows in the contrast matrices correspond to
## factor levels in the order that they first appear in the @var{GROUP} column.
## The matrix must contain the same number of columns as there are the number of
## factor levels minus one.
## @end itemize
##
## If the anovan model contains more than one factor and a built-in contrast
## coding scheme was specified, then those contrasts are applied to all factors.
## To specify different contrasts for different factors in the model,
## @var{contrasts} should be a cell array with the same number of cells as there
## are columns in @var{GROUP}. Each cell should define contrasts for the
## respective column in @var{GROUP} by one of the methods described above. If
## cells are left empty, then the default contrasts are applied. Contrasts for
## cells corresponding to continuous factors are ignored.
## @end itemize
##
## @code{[@dots{}] = anovan (@var{Y}, @var{GROUP}, "weights", @var{weights})}
##
## @itemize
## @item
## @var{weights} is an optional vector of weights to be used when fitting the
## linear model. Weighted least squares (WLS) is used with weights (that is,
## minimizing @code{sum (@var{weights} * @var{residuals} .^ 2))}; otherwise
## ordinary least squares (OLS) is used (default is empty for OLS).
## @end itemize
##
## @code{anovan} can return up to four output arguments:
##
## @code{@var{p} = anovan (@dots{})} returns a vector of p-values, one for each
## term.
##
## @code{[@var{p}, @var{atab}] = anovan (@dots{})} returns a cell array
## containing the ANOVA table.
##
## @code{[@var{p}, @var{atab}, @var{stats}] = anovan (@dots{})} returns a
## structure containing additional statistics, including degrees of freedom and
## effect sizes for each term in the linear model, the design matrix, the
## variance-covariance matrix, (weighted) model residuals, and the mean squared
## error. The columns of @var{stats}.coeffs (from left-to-right) report the
## model coefficients, standard errors, lower and upper @math{100*(1-alpha)%}
## confidence interval bounds, t-statistics, and p-values relating to the
## contrasts. The number appended to each term name in @var{stats}.coeffnames
## corresponds to the column number in the relevant contrast matrix for that
## factor. The @var{stats} structure can be used as input for @code{multcompare}.
##
## @code{[@var{p}, @var{atab}, @var{stats}, @var{terms}] = anovan (@dots{})}
## returns the model term definitions.
##
## @seealso{anova1, anova2, multcompare, fitlm}
## @end deftypefn

function [P, T, STATS, TERMS] = anovan (Y, GROUP, varargin)

    if (nargin < 2)
      error (strcat (["anovan usage: ""anovan (Y, GROUP)""; "], ...
                      [" atleast 2 input arguments required"]));
    endif

    ## Check supplied parameters
    if ((numel (varargin) / 2) != fix (numel (varargin) / 2))
      error ("anovan: wrong number of arguments.")
    endif
    MODELTYPE = "linear";
    DISPLAY = "on";
    SSTYPE = 3;
    VARNAMES = [];
    CONTINUOUS = [];
    RANDOM = [];
    CONTRASTS = {};
    ALPHA = 0.05;
    WEIGHTS = [];
    for idx = 3:2:nargin
      name = varargin{idx-2};
      value = varargin{idx-1};
      switch (lower (name))
        case "model"
          MODELTYPE = value;
        case "continuous"
          CONTINUOUS = value;
        case "random"
          RANDOM = value;
        case "nested"
          error (strcat (["anovan: nested ANOVA is not supported. Please use"], ...
                         [" anova2 for fully balanced nested ANOVA designs."]));
        case "sstype"
          SSTYPE = value;
        case "varnames"
          VARNAMES = value;
        case {"display","displayopt"}
          DISPLAY = value;
        case "contrasts"
          CONTRASTS = value;
        case "alpha"
          ALPHA = value;
        case "weights"
          WEIGHTS = value;
        otherwise
          error (sprintf ("anovan: parameter %s is not supported", name));
      endswitch
    endfor

    ## Evaluate continuous input argument
    if (isnumeric (CONTINUOUS))
      if (any (CONTINUOUS != abs (fix (CONTINUOUS))))
        error (strcat (["anovan: the value provided for the CONTINUOUS"], ...
                       [" parameter must be a positive integer"]));
      endif
    else
      error (strcat (["anovan: the value provided for the CONTINUOUS"], ...
                     [" parameter must be numeric"]));
    endif

    ## Accommodate for different formats for GROUP
    ## GROUP can be a matrix of numeric identifiers of a cell arrays
    ## of strings or numeric identifiers
    N = size (GROUP, 2); # number of anova "ways"
    n = numel (Y);       # total number of observations
    if (prod (size (Y)) != n)
      error ("anovan: for ""anovan (Y, GROUP)"", Y must be a vector");
    endif
    if (numel (unique (CONTINUOUS)) > N)
      error (strcat (["anovan: the number of factors assigned as continuous"], ...
                 [" cannot exceed the number of factors in GROUP"]));
    endif
    if (any ((CONTINUOUS > N) || any (CONTINUOUS <= 0)))
      error (strcat (["anovan: one or more indices provided in the value"], ...
                 [" for the continuous parameter are out of range"]));
    endif
    cont_vec = false (1, N);
    cont_vec(CONTINUOUS) = true;
    if (iscell (GROUP))
      if (size (GROUP, 1) == 1)
        tmp = cell (n, N);
        for j = 1:N
          if (isnumeric (GROUP{j}))
            if (ismember (j, CONTINUOUS))
              tmp(:,j) = num2cell (GROUP{j});
            else
              tmp(:,j) = cellstr (num2str (GROUP{j}));
            endif
          else
            if (ismember (j, CONTINUOUS))
              error ("anovan: continuous factors must be a numeric datatype");
            endif
            tmp(:,j) = GROUP{j};
          endif
        endfor
        GROUP = tmp;
      endif
    endif
    if (! isempty (GROUP))
      if (size (GROUP,1) != n)
        error ("anovan: GROUP must be a matrix with the same number of rows as Y");
      endif
    endif
    if (! isempty (VARNAMES))
      if (iscell (VARNAMES))
        if (all (cellfun (@ischar, VARNAMES)))
          nvarnames = numel(VARNAMES);
        else
          error (strcat (["anovan: all variable names must be character"], ...
                         [" or character arrays"]));
        endif
      elseif (ischar (VARNAMES))
        nvarnames = 1;
        VARNAMES = {VARNAMES};
      elseif (isstring (VARNAMES))
        nvarnames = 1;
        VARNAMES = {char(VARNAMES)};
      else
        error (strcat (["anovan: varnames is not of a valid type. Must be a cell"], ...
               [" array of character arrays, character array or string"]));
      endif
    else
      nvarnames = N;
      VARNAMES = arrayfun(@(x) ["X",num2str(x)], 1:N, "UniformOutput", 0);
    endif
    if (nvarnames != N)
      error (strcat (["anovan: number of variable names is not equal"], ...
                     [" to the number of grouping variables"]));
    endif

    ## Evaluate random argument (if applicable)
    if (! isempty(RANDOM))
      if (isnumeric (RANDOM))
        if (any (RANDOM != abs (fix (RANDOM))))
          error (strcat (["anovan: the value provided for the RANDOM"], ...
                         [" parameter must be a positive integer"]));
        endif
      else
        error (strcat (["anovan: the value provided for the RANDOM"], ...
                     ["  parameter must be numeric"]));
      endif
      if (numel (RANDOM) > N)
        error (strcat (["anovan: the number of elements in RANDOM cannot"], ...
               [" exceed the number of columns in GROUP."]));
      endif
      if (max (RANDOM) > N)
        error (strcat (["anovan: the indices listed in RANDOM cannot"], ...
               [" exceed the number of columns in GROUP."]));
      endif
      for v = 1:N
        if (ismember (v, RANDOM))
          VARNAMES{v} = strcat (VARNAMES{v},"'");
        endif
      endfor
    endif

    ## Evaluate contrasts (if applicable)
    if isempty (CONTRASTS)
      CONTRASTS = cell (1, N);
      planned = false;
    else
      if (ischar(CONTRASTS))
        contr_str = CONTRASTS;
        CONTRASTS = cell (1, N);
        CONTRASTS(:) = {contr_str};
      endif
      if (! iscell (CONTRASTS))
        CONTRASTS = {CONTRASTS};
      endif
      for i = 1:N
        if (! isempty (CONTRASTS{i}))
          msg = strcat(["columns in CONTRASTS must sum to"], ...
                       [" 0 for SSTYPE 3. Switching to SSTYPE 2 instead."]);
          if (isnumeric(CONTRASTS{i}))
            ## Check whether all the columns sum to 0
            if (any (abs (sum (CONTRASTS{i})) > eps ('single')))
              warning (sprintf ( ...
              'Note that the CONTRASTS for predictor %u do not sum to zero', i));
            endif
            ## Check whether contrasts are orthogonal
            if (any (abs (reshape (corr (CONTRASTS{i}) - ...
                                          eye (size (CONTRASTS{i}, 2)), [], 1))...
                                        > eps ('single')))
              warning (sprintf ( ...
              'Note that the CONTRASTS for predictor %u are not orthogonal', i));
            endif
          else
            if (! ismember (lower (CONTRASTS{i}), ...
                            {"simple","anova","poly","helmert","effect",...
                              "sdif","sdiff","treatment"}))
              error (strcat(["anovan: valid built-in contrasts are:"], ...
                            [" ""simple"", ""poly"", ""helmert"","],...
                            ["""effect"", ""sdif"" or ""treatment"""]));
            endif
            if (strcmpi (CONTRASTS{i}, "treatment") && (SSTYPE==3))
              warning (msg);
              SSTYPE = 2;
            endif
          endif
        endif
      endfor
      planned = true;
    endif

    ## Evaluate alpha input argument
    if (! isa (ALPHA,'numeric') || numel (ALPHA) != 1)
      error("anovan: alpha must be a numeric scalar value");
    endif
    if ((ALPHA <= 0) || (ALPHA >= 1))
      error("anovan: alpha must be a value between 0 and 1");
    endif

    ## Remove NaN or non-finite observations
    if (isempty (GROUP))
      excl = any ([isnan(Y), isinf(Y)], 2);
    else
      XC = GROUP(:,CONTINUOUS);
      if iscell(XC)
        XC = cell2mat (XC);
      endif
      excl = any ([isnan(Y), isinf(Y), any(isnan(XC),2), any(isinf(XC),2)], 2);
      GROUP(excl,:) = [];
    endif
    Y(excl) = [];
    if (size (Y, 1) == 1)
      Y = Y.';         # if Y is a row vector, make it a column vector
    endif
    n = numel (Y);     # recalculate total number of observations

    ## Evaluate weights input argument
    if (! isempty(WEIGHTS))
      if (! isnumeric(WEIGHTS))
        error ("anovan: WEIGHTS must be a numeric datatype");
      endif
      if (any (size (WEIGHTS) != [n,1]))
        error ("anovan: WEIGHTS must be a vector with the same dimensions as Y");
      endif
      if (any(!(WEIGHTS > 0)) || any (isinf (WEIGHTS)))
        error ("anovan: WEIGHTS must be a vector of positive finite values");
      endif
      # Create diagonal matrix of normalized weights
      W = diag (WEIGHTS / mean (WEIGHTS));
    else
      # Create identity matrix
      W = eye (n);;
    endif

    ## Evaluate model type input argument and create terms matrix if not provided
    msg = strcat (["anovan: the number of columns in the term definitions"], ...
                  [" cannot exceed the number of columns of GROUP"]);
    if (ischar (MODELTYPE))
      switch (lower (MODELTYPE))
        case "linear"
          MODELTYPE = 1;
        case {"interaction","interactions"}
          MODELTYPE = 2;
        case "full"
          MODELTYPE = N;
        otherwise
          error ("anovan: model type not recognised");
      endswitch
    endif
    if (isscalar (MODELTYPE))
      TERMS = cell (MODELTYPE,1);
      v = false (1, N);
      switch (lower (MODELTYPE))
        case 1
          ## Create term definitions for an additive linear model
          TERMS = eye (N);
        case 2
          ## Create term definitions for a model with two factor interactions
          if (N > 1)
            Nx = nchoosek (N, 2);
          else
            Nx = 0;
          endif
          TERMS = zeros (N + Nx, N);
          TERMS(1:N,:) = eye (N);
          cnt = N + 1;
          for j = 1:N
            for i = j:N-1
              TERMS(cnt,j) = 1;
              TERMS(cnt,i+1) = 1;
              cnt++;
            endfor
          endfor
        otherwise
          if (MODELTYPE > N)
            error (msg);
          endif
          ## Create term definitions for a full model
          Nx = zeros (1, N-1);
          Nx = 0;
          for k = 1:N
            Nx = Nx + nchoosek(N,k);
          endfor
          for j = 1:MODELTYPE
            v(1:j) = 1;
            TERMS(j) = flipud (unique (perms (v), "rows"));
          endfor
          TERMS = cell2mat (TERMS);
      endswitch
      TERMS = logical (TERMS);
    else
      ## Assume that the user provided a suitable matrix of term definitions
      if (size (MODELTYPE, 2) > N)
        error (msg);
      endif
      if (! all (ismember (MODELTYPE(:), [0,1])))
        error (strcat (["anovan: elements of the model terms matrix"], ...
                       [" must be either 0 or 1"]));
      endif
      TERMS = logical (MODELTYPE);
    endif
    ## Evaluate terms matrix
    Ng = sum (TERMS, 2);
    if (any (diff (Ng) < 0))
      error (strcat (["anovan: the model terms matrix must list main"], ...
                     [" effects above/before interactions"]));
    endif
    ## Drop terms that include interactions with factors specified as random effects.
    drop = any (bsxfun (@and, TERMS(:,RANDOM), (Ng > 1)), 2);
    TERMS (drop, :) = [];
    Ng(drop) = [];
    ## Evaluate terms
    Nm = sum (Ng == 1);
    Nx = sum (Ng > 1);
    Nt = Nm + Nx;
    if (any (any (TERMS(1:Nm,:), 1) != any (TERMS, 1)))
      error (strcat (["anovan: all factors involved in interactions"], ...
                     [" must have a main effect"]));
    endif

    ## Calculate total sum-of-squares
    ct  = sum (Y)^2 / n;   % correction term
    sst = sum (Y.^2) - ct;
    dft = n - 1;

    ## Create design matrix
    mDesignMatrix ();

    ## Fit linear models, and calculate sums-of-squares for ANOVA
    switch (lower (SSTYPE))
      case 1
        ## Type I sequential sums-of-squares (SSTYPE = 1)
        R = sst;
        ss = zeros (Nt,1);
        for j = 1:Nt
          XS = cell2mat (X(1:j+1));
          [b, sse] = lmfit (XS, Y, W);
          ss(j) = R - sse;
          R = sse;
        endfor
        [b, sse, resid, ucov, hat] = lmfit (XS, Y, W);
        sstype_char = "I";
      case {2,'h'}
        ## Type II (partially sequential, or hierarchical) sums-of-squares
        ss = zeros (Nt,1);
        for j = 1:Nt
          i = find (TERMS(j,:));
          k = cat (1, 1, 1 + find (any (!TERMS(:,i),2)));
          XS = cell2mat (X(k));
          [jnk, R1] = lmfit (XS, Y, W);
          k = cat (1, j+1, k);
          XS = cell2mat (X(k));
          [jnk, R2] = lmfit (XS, Y, W);
          ss(j) = R1 - R2;
        endfor
        [b, sse, resid, ucov, hat] = lmfit (cell2mat (X), Y, W);
        sstype_char = "II";
      case 3
        ## Type III (partial, constrained or marginal) sums-of-squares
        ss = zeros (Nt, 1);
        [b, sse, resid, ucov, hat] = lmfit (cell2mat (X), Y, W);
        for j = 1:Nt
          XS = cell2mat (X(1:Nt+1 != j+1));
          [jnk, R] = lmfit (XS, Y, W);
          ss(j) = R - sse;
        endfor
        sstype_char = "III";
      otherwise
        error ("anovan: sstype value not supported");
    endswitch
    ss = max (0, ss); # Truncate negative SS at 0
    dfe = dft - sum (df);
    ms = ss ./ df;
    mse = sse / dfe;
    eta_sq = ss ./ sst;
    partial_eta_sq = ss ./ (ss + sse);
    F = ms / mse;
    P = 1 - fcdf (F, df, dfe);

    ## Prepare model formula and cell array containing the ANOVA table
    T = cell (Nt + 3, 7);
    T(1,:) = {"Source", "Sum Sq.", "d.f.", "Mean Sq.", "Eta Sq.", "F", "Prob>F"};
    T(2:Nt+1,2:7) = num2cell ([ss df ms partial_eta_sq F P]);
    T(end-1,1:4) = {"Error", sse, dfe, mse};
    T(end,1:3) = {"Total", sst, dft};
    formula = sprintf ("Y ~ 1");  # Initialize model formula
    for i = 1:Nt
      str = sprintf ("%s*", VARNAMES{find (TERMS(i,:))});
      T(i+1,1) = str(1:end-1);
      ## Append model term to formula
      str = regexprep (str, "\\*", ":");
      if (strcmp (str(end-1), "'"))
        ## Random intercept term
        formula = sprintf ("%s + (1|%s)", formula, str(1:end-2));
        ## Remove statistics for random factors from the ANOVA table
        #T(RANDOM+1,4:7) = cell(1,4);
        #P(RANDOM) = NaN;
      else
        ## Fixed effect term
        formula = sprintf ("%s + %s", formula, str(1:end-1));
      endif
    endfor

    ## Calculate a standard error, t-statistic and p-value for each
    ## of the regression coefficients (fixed effects only)
    t_crit = tinv (1 - ALPHA / 2, dfe);
    se = sqrt (diag (ucov) * mse);
    t =  b ./ se;
    p = 2 * (1 - (tcdf (abs (t), dfe)));
    coeff_stats = zeros (1 + sum (df), 4);
    coeff_stats(:,1) = b;                                # coefficients
    coeff_stats(:,2) = se;                               # standard errors
    coeff_stats(:,3) = b - se * t_crit;                  # Lower CI bound
    coeff_stats(:,4) = b + se * t_crit;                  # Upper CI bound
    coeff_stats(:,5) = t;                                # t-statistics
    coeff_stats(:,6) = p;                                # p-values
    ## Assign NaN to p-value to avoid printing statistics relating to
    ## coefficients for 'random' effects
    hi = 1 + cumsum(df);
    for ignore = RANDOM
      p(hi(ignore)-df(ignore)+1:hi(ignore)) = NaN;
    endfor

    ## Compute leverage values and Cook's distance
    h = diag (hat);          % Leverage values
    D = resid.^2 / ((1 + sum (df)) * mse) ...
        .* h ./ (1 - h).^2;  % Cook's distance

    ## Create STATS structure for MULTCOMPARE
    STATS = struct ("source","anovan", ...
                    "resid", resid, ...      # These are weighted (not raw) residuals
                    "coeffs", coeff_stats, ...
                    "Rtr", [], ...           # Not used by Octave
                    "rowbasis", [], ...      # Not used by Octave
                    "dfe", dfe, ...
                    "mse", mse, ...
                    "nullproject", [], ...   # Not used by Octave
                    "terms", TERMS, ...
                    "nlevels", nlevels, ...
                    "continuous", cont_vec, ...
                    "vmeans", vmeans, ...
                    "termcols", termcols, ...
                    "coeffnames", {cellstr(char(coeffnames{:}))}, ...
                    "vars", [], ...          # Not used by Octave
                    "varnames", {VARNAMES}, ...
                    "grpnames", {levels}, ...
                    "vnested", [], ...       # Not used since "nested" argument name is not supported
                    "ems", [], ...           # Not used since "nested" argument name is not supported
                    "denom", [], ...         # Not used since interactions with random factors is not supported
                    "dfdenom", [], ...       # Not used since interactions with random factors is not supported
                    "msdenom", [], ...       # Not used since interactions with random factors is not supported
                    "varest", [], ...        # Not used since interactions with random factors is not supported
                    "varci", [], ...         # Not used since interactions with random factors is not supported
                    "txtdenom", [], ...      # Not used since interactions with random factors is not supported
                    "txtems", [], ...        # Not used since interactions with random factors is not supported
                    "rtnames", [], ...       # Not used since interactions with random factors is not supported
                    ## Additional STATS fields used exclusively by Octave
                    "center_continuous", center_continuous, ...
                    "random", RANDOM, ...
                    "formula", formula, ...
                    "alpha", ALPHA, ...
                    "df", df, ...
                    "contrasts", {CONTRASTS}, ...
                    "X", sparse (cell2mat (X)), ...
                    "Y", Y, ...
                    "W", sparse (W), ...
                    "lmfit", @lmfit, ...
                    "vcov", sparse (ucov * mse), ...
                    "CooksD", D, ...
                    "grps", gid, ...
                    "eta_squared", eta_sq, ...
                    "partial_eta_squared", partial_eta_sq);

    ## Print ANOVA table
    switch (lower (DISPLAY))

      case {"on", true}

        ## Print model formula
        fprintf("\nMODEL FORMULA (based on Wilkinson's notation):\n\n%s\n", formula);

        ## If applicable, print parameter estimates (a.k.a contrasts) for fixed effects
        if (planned && ! isempty(GROUP))
          ## Parameter estimates correspond to the contrasts we set. To avoid
          ## p-hacking, don't print contrasts if we don't specify them to start with
          fprintf("\nMODEL PARAMETERS (contrasts for the fixed effects)\n\n");
          fprintf("Parameter               Estimate        SE  Lower.CI  Upper.CI        t Prob>|t|\n");
          fprintf("--------------------------------------------------------------------------------\n");
          for j = 1:size (coeff_stats, 1)
            if (p(j) < 0.001)
              fprintf ("%-20s  %10.3g %9.3g %9.3g %9.3g %8.2f    <.001 \n", ...
                       STATS.coeffnames{j}, STATS.coeffs(j,1:end-1));
            elseif (p(j) < 0.9995)
              fprintf ("%-20s  %10.3g %9.3g %9.3g %9.3g %8.2f     .%03u \n", ...
                       STATS.coeffnames{j}, STATS.coeffs(j,1:end-1), round (p(j) * 1e+03));
            elseif (isnan(p(j)))
              ## Don't display coefficients for 'random' effects since they were
              ## treated as fixed effects
            else
              fprintf ("%-20s  %10.3g %9.3g %9.3g %9.3g %8.2f    1.000 \n", ...
                       STATS.coeffnames{j}, STATS.coeffs(j,1:end-1));
            endif
          endfor
        endif

        ## Print ANOVA table
        [nrows, ncols] = size (T);
        fprintf("\nANOVA TABLE (Type %s sums-of-squares):\n\n", sstype_char);
        fprintf("Source                   Sum Sq.    d.f.    Mean Sq.  R Sq.            F  Prob>F\n");
        fprintf("--------------------------------------------------------------------------------\n");
        for i = 1:Nt
          str = T{i+1,1};
          l = numel(str);  # Needed to truncate source term name at 18 characters
          ## Format and print the statistics for each model term
          ## Format F statistics and p-values in APA style
          if (P(i) < 0.001)
            fprintf ("%-20s  %10.5g  %6d  %10.5g  %4.3f  %11.2f   <.001 \n", ...
                      str(1:min(18,l)), T{i+1,2:end-1});
          elseif (P(i) < 0.9995)
            fprintf ("%-20s  %10.5g  %6d  %10.5g  %4.3f  %11.2f    .%03u \n", ...
                      str(1:min(18,l)), T{i+1,2:end-1}, round (P(i) * 1e+03));
          elseif (isnan(P(i)))
            fprintf ("%-20s  %10.5g  %6d \n", str(1:min(18,l)), T{i+1,2:3});
          else
            fprintf ("%-20s  %10.5g  %6d  %10.5g  %4.3f  %11.2f    1.000 \n", ...
                      str(1:min(18,l)), T{i+1,2:end-1});
          endif
        endfor
        fprintf("Error                 %10.5g  %6d  %10.5g\n", T{end-1,2:4});
        fprintf("Total                 %10.5g  %6d \n", T{end,2:3});
        fprintf("\n");

        ## Make figure of diagnostic plots
        figure ("Name", "Diagnostic Plots: Model Residuals");
        t = STATS.resid ./ (sqrt (mse * (1 - h))); % Studentized residuals
        fit = STATS.X * STATS.coeffs(:,1);         % Fitted values
        [jnk, DI] = sort (D, "descend");           % Indices of sorted D
        nk = 4;                               % Top nk residuals with largest D

        ## Normal quantile-quantile plot
        subplot (2, 2, 1);
        x = ((1 : n)' - .5) / n;
        [ts, I] = sort (t);
        q = norminv (x);
        plot (q, ts, "ok", "markersize", 3);
        box off;
        grid on;
        xlabel ("Theoretical quantiles");
        ylabel ("Studentized Residuals");
        title ("Normal Q-Q Plot");
        arrayfun (@(i) text (q(I == DI(i)), t(DI(i)), ...
                             sprintf ("  %u", DI(i))), [1:min(nk,n)])
        iqr = [0.25; 0.75];
        yl = quantile (t, iqr, 1, 6);
        xl = norminv (iqr);
        slope = diff (yl) / diff (xl);
        int = yl(1) - slope * xl(1);
        ax1_xlim = get (gca, "XLim");
        hold on; plot (ax1_xlim, slope * ax1_xlim + int, "k-"); hold off;
        set (gca, "Xlim", ax1_xlim);

        ## Spread-Location Plot
        subplot (2, 2, 2);
        plot (fit, sqrt (abs (t)), "ko", "markersize", 3);
        box off;
        xlabel ("Fitted values");
        ylabel ("sqrt ( | Studentized Residuals | )");
        title ("Spread-Location Plot")
        ax2_xlim = get (gca, "XLim");
        hold on;
        plot (ax2_xlim, ones (1, 2) * sqrt (2), "k:");
        plot (ax2_xlim, ones (1, 2) * sqrt (3), "k-.");
        plot (ax2_xlim, ones (1, 2) * sqrt (4), "k--");
        hold off;
        arrayfun (@(i) text (fit(DI(i)), sqrt (abs (t(DI(i)))), ...
                             sprintf ("  %u", DI(i))), [1:min(nk,n)]);
        xlim (ax2_xlim);

        ## Residual-Leverage plot
        subplot (2, 2, 3);
        plot (h, t, "ko", "markersize", 3);
        box off;
        xlabel ("Leverage")
        ylabel ("Studentized Residuals");
        title ("Residual-Leverage Plot")
        ax3_xlim = get (gca, "XLim");
        ax3_ylim = get (gca, "YLim");
        hold on; plot (ax3_xlim, zeros (1, 2), "k-"); hold off;
        arrayfun (@(i) text (h(DI(i)), t(DI(i)), ...
                             sprintf ("  %u", DI(i))), [1:min(nk,n)]);
        set (gca, "ygrid", "on");
        xlim (ax3_xlim); ylim (ax3_ylim);

        ## Cook's distance stem plot
        subplot (2, 2, 4);
        stem (D, "ko", "markersize", 3);
        box off;
        xlabel ("Obs. number")
        ylabel ("Cook's distance")
        title ("Cook's Distance Stem Plot")
        xlim ([0, n]);
        ax4_xlim = get (gca, "XLim");
        ax4_ylim = get (gca, "YLim");
        hold on;
        plot (ax4_xlim, ones (1, 2) * 4 / dfe, "k:");
        plot (ax4_xlim, ones (1, 2) * 0.5, "k-.");
        plot (ax4_xlim, ones (1, 2), "k--");
        hold off;
        arrayfun (@(i) text (DI(i), D(DI(i)), ...
                             sprintf ("  %u", DI(i))), [1:min(nk,n)]);
        xlim (ax4_xlim); ylim (ax4_ylim);

        set (findall ( gcf, "-property", "FontSize"), "FontSize", 7)

      case {"off", false}

        ## do nothing

      otherwise

        error ("anovan: wrong value for 'display' parameter.");

    endswitch

    function mDesignMatrix ()

      ## Nested function that returns a cell array of the design matrix for
      ## each term in the model
      ## Input variables it uses:
      ##  GROUP, TERMS, CONTINUOUS, CONTRASTS, VARNAMES, n, Nm, Nx, Ng
      ## Variables it creates or modifies:
      ##  X, grpnames, nlevels, df, termcols, coeffnames, vmeans, gid, CONTRASTS

      ## EVALUATE FACTOR LEVELS
      levels = cell (Nm, 1);
      gid = zeros (n, Nm);
      nlevels = zeros (Nm, 1);
      df = zeros (Nm + Nx, 1);
      termcols = ones (1 + Nm + Nx, 1);
      for j = 1:Nm
        if (any (j == CONTINUOUS))

          ## CONTINUOUS PREDICTOR
          nlevels(j) = 1;
          termcols(j+1) = 1;
          df(j) = 1;
          if iscell (GROUP(:,j))
            gid(:,j) = cell2mat ([GROUP(:,j)]);
          else
            gid(:,j) = GROUP(:,j);
          end

        else

          ## CATEGORICAL PREDICTOR
          levels{j} = unique (GROUP(:,j), "stable");
          if isnumeric (levels{j})
            levels{j} = num2cell (levels{j});
          endif
          nlevels(j) = numel (levels{j});
          for k = 1:nlevels(j)
            gid(ismember (GROUP(:,j),levels{j}{k}),j) = k;
          endfor
          termcols(j+1) = nlevels(j);
          df(j) = nlevels(j) - 1;

        endif
      endfor

      ## MAKE DESIGN MATRIX

      ## MAIN EFFECTS
      X = cell (1, 1 + Nm + Nx);
      X(1) = ones (n, 1);
      coeffnames = cell (1, 1 + Nm + Nx);
      coeffnames(1) = "(Intercept)";
      vmeans = zeros (Nm, 1);
      center_continuous = cont_vec;
      for j = 1:Nm
        if (any (j == CONTINUOUS))

          ## CONTINUOUS PREDICTOR
          if iscell (GROUP(:,j))
            X(1+j) = cell2mat (GROUP(:,j));
          else
            X(1+j) = GROUP(:,j);
          end
          if (strcmpi (CONTRASTS{j}, 'treatment'))
            ## Don't center continuous variables if contrasts are 'treatment'
            center_continuous(j) = false;
            CONTRASTS{j} = [];
          else
            center_continuous(j) = true;
            vmeans(j) = mean ([X{1+j}]);
            X(1+j) = [X{1+j}] - vmeans(j);
          endif
          ## Create names of the coefficients relating to continuous main effects
          coeffnames{1+j} = VARNAMES{j};

        else

          ## CATEGORICAL PREDICTOR
          if (isempty (CONTRASTS{j}))
            CONTRASTS{j} = contr_simple (nlevels(j));
          else
            switch (lower (CONTRASTS{j}))
              case {"simple","anova"}
                ## SIMPLE EFFECT CODING (DEFAULT)
                ## The first level is the reference level
                CONTRASTS{j} = contr_simple (nlevels(j));
              case "poly"
                ## POLYNOMIAL CONTRAST CODING
                CONTRASTS{j} = contr_poly (nlevels(j));
              case "helmert"
                ## HELMERT CONTRAST CODING
                CONTRASTS{j} = contr_helmert (nlevels(j));
              case "effect"
                ## DEVIATION EFFECT CONTRAST CODING
                CONTRASTS{j} = contr_sum (nlevels(j));
              case {"sdif","sdiff"}
                ## SUCCESSIVE DEVIATIONS CONTRAST CODING
                CONTRASTS{j} = contr_sdif (nlevels(j));
              case "treatment"
                ## The first level is the reference level
                CONTRASTS{j} = contr_treatment (nlevels(j));
              otherwise
                ## EVALUATE CUSTOM CONTRAST MATRIX
                ## Check that the contrast matrix provided is the correct size
                if (! all (size (CONTRASTS{j},1) == nlevels(j)))
                  error (strcat (["anovan: the number of rows in the contrast"], ...
                               [" matrices should equal the number of factor levels"]));
                endif
                if (! all (size (CONTRASTS{j},2) == df(j)))
                  error (strcat (["anovan: the number of columns in each contrast"], ...
                          [" matrix should equal the degrees of freedom (i.e."], ...
                          [" number of levels minus 1) for that factor"]));
                endif
                if (! all (any (CONTRASTS{j})))
                  error (strcat (["anovan: a contrast must be coded in each"], ...
                                 [" column of the contrast matrices"]));
                endif
            endswitch
          endif
          C = CONTRASTS{j};
          func = @(x) x(gid(:,j));
          X(1+j) = cell2mat (cellfun (func, num2cell (C, 1), "UniformOutput", false));
          ## Create names of the coefficients relating to continuous main effects
          coeffnames{1+j} = cell (df(j),1);
          for v = 1:df(j)
            coeffnames{1+j}{v} = sprintf ("%s_%u", VARNAMES{j}, v);
          endfor

        endif
      endfor

      ## INTERACTION TERMS
      if (Nx > 0)
        row = TERMS((Ng > 1),:);
        for i = 1:Nx
          I = 1 + find (row(i,:));
          df(Nm+i) = prod (df(I-1));
          termcols(1+Nm+i) = prod (df(I-1) + 1);
          tmp = ones (n,1);
          for j = 1:numel(I);
            tmp = num2cell (tmp, 1);
            for k = 1:numel(tmp)
              tmp(k) = bsxfun (@times, tmp{k}, X{I(j)});
            endfor
            tmp = cell2mat (tmp);
          endfor
          X{1+Nm+i} = tmp;
          coeffnames{1+Nm+i} = cell (df(Nm+i),1);
          for v = 1:df(Nm+i)
            str = sprintf ("%s:", VARNAMES{I-1});
            coeffnames{1+Nm+i}{v} = strcat (str(1:end-1), "_", num2str (v));
          endfor
        endfor
      endif

    endfunction

endfunction

## BUILT IN CONTRAST CODING FUNCTIONS

function C = contr_simple (N)

  ## Create contrast matrix (of doubles) using simple (ANOVA) contrast coding
  ## These contrasts are centered (i.e. sum to 0)
  ## Ideal for unordered factors, with comparison to a reference level
  ## The first factor level is the reference level
  C =  cat (1, zeros (1,N-1), eye(N-1)) - 1/N;

endfunction

function C = contr_poly (N)

  ## Create contrast matrix (of doubles) using polynomial contrast coding
  ## for trend analysis of ordered categorical factor levels
  ## These contrasts are orthogonal and centered (i.e. sum to 0)
  ## Ideal for ordered factors
  [C, jnk] = qr (bsxfun (@power, [1:N]' - mean ([1:N]'), [0:N-1]));
  C(:,1) = [];
  s = ones (1, N-1);
  s(1:2:N-1) *= -1;
  f = (sign(C(1,:)) != s);
  C(:,f) *= -1;

endfunction

function C = contr_helmert (N)

  ## Create contrast matrix (of doubles) using Helmert coding contrasts
  ## These contrasts are orthogonal and centered (i.e. sum to 0)
  C = cat (1, tril (-ones (N-1), -1) + diag (N-1:-1:1), ...
              -ones (1, N-1)) ./ (N:-1:2);

endfunction

function C = contr_sum (N)

  ## Create contrast matrix (of doubles) using deviation effect coding
  ## These contrasts are centered (i.e. sum to 0)
  C =  cat (1, - (ones (1,N-1)), eye (N-1));

endfunction

function C = contr_sdif (N)

  ## Create contrast matrix (of doubles) using successive differences coding
  ## These contrasts are centered (i.e. sum to 0)
  C =  tril (ones (N, N - 1), -1) - ones (N, 1) / N * [N - 1 : -1 : 1];

endfunction

function C = contr_treatment (N)

  ## Create contrast matrix (of doubles) using treatment contrast coding
  ## Not compatible with SSTYPE 3 since contrasts are not centered
  ## Ideal for unordered factors, with comparison to a reference level
  ## The first factor level is the reference level
  C =  cat (1, zeros (1,N-1), eye(N-1));

endfunction


## FUNCTION TO FIT THE LINEAR MODEL

function [b, sse, resid, ucov, hat] = lmfit (X, Y, W)

  ## Get model coefficients by solving the linear equation by QR decomposition
  ## The number of free parameters (i.e. intercept + coefficients) is equal
  ## to n - dfe. If optional arument W is provided, it should be a diagonal
  ## matrix of weights or a positive definite covariance matrix
  if (nargin < 3)
    ## If no weights are provided, create an identity matrix
    n = numel (Y);
    W = eye (n);
  endif
  C = chol (W);
  XW = C*X;
  YW = C*Y;
  [Q, R] = qr (XW, 0);
  b = R \ Q' * YW;

  ## Get fitted values
  fit = Q'\R * b;  # This is equivalent to fit = XW * b;

  ## Get residuals from the fit
  resid = YW - fit;

  ## Calculate the residual sums-of-squares
  sse = sum (resid.^2);

  ## Calculate the unscaled covariance matrix (i.e. inv (X'*X ))
  if (nargout > 3)
    ucov = R \ Q' / XW';
  endif

  ## Calculate the Hat matrix
  if (nargout > 4)
    w = diag (W);
    rw = sqrt (w);
    Q1 = diag (1 ./ rw) * Q;
    Q2 = diag (rw) * Q;
    hat = Q1 * Q2';
  endif

endfunction

%!demo
%!
%! # Two-sample unpaired test on independent samples (equivalent to Student's
%! # t-test). Note that the absolute value of t-statistic can be obtained by
%! # taking the square root of the reported F statistic. In this example,
%! # t = sqrt (1.44) = 1.20.
%!
%! score = [54 23 45 54 45 43 34 65 77 46 65]';
%! gender = {"male" "male" "male" "male" "male" "female" "female" "female" ...
%!           "female" "female" "female"}';
%!
%! [P, ATAB, STATS] = anovan (score, gender, "display", "on", "varnames", "gender");

%!demo
%!
%! # Two-sample paired test on dependent or matched samples equivalent to a
%! # paired t-test. As for the first example, the t-statistic can be obtained by
%! # taking the square root of the reported F statistic. Note that the interaction
%! # between treatment x subject was dropped from the full model by assigning
%! # subject as a random factor (').
%!
%! score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
%! treatment = {"before" "after"; "before" "after"; "before" "after";
%!              "before" "after"; "before" "after"}';
%! subject = {"GS" "GS"; "JM" "JM"; "HM" "HM"; "JW" "JW"; "PS" "PS"}';
%!
%! [P, ATAB, STATS] = anovan (score(:), {treatment(:), subject(:)}, ...
%!                            "model", "full", "random", 2, "sstype", 2, ...
%!                            "varnames", {"treatment", "subject"}, ...
%!                            "display", "on");

%!demo
%!
%! # One-way ANOVA on the data from a study on the strength of structural beams,
%! # in Hogg and Ledolter (1987) Engineering Statistics. New York: MacMillan
%!
%! strength = [82 86 79 83 84 85 86 87 74 82 ...
%!            78 75 76 77 79 79 77 78 82 79]';
%! alloy = {"st","st","st","st","st","st","st","st", ...
%!          "al1","al1","al1","al1","al1","al1", ...
%!          "al2","al2","al2","al2","al2","al2"}';
%!
%! [P, ATAB, STATS] = anovan (strength, alloy, "display", "on", ...
%!                            "varnames", "alloy");

%!demo
%!
%! # One-way repeated measures ANOVA on the data from a study on the number of
%! # words recalled by 10 subjects for three time conditions, in Loftus & Masson
%! # (1994) Psychon Bull Rev. 1(4):476-490, Table 2. Note that the interaction
%! # between seconds x subject was dropped from the full model by assigning
%! # subject as a random factor (').
%!
%! words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
%!          15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
%! seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
%!            1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
%! subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
%!             6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
%!
%! [P, ATAB, STATS] = anovan (words(:), {seconds(:), subject(:)}, ...
%!                            "model", "full", "random", 2, "sstype", 2, ...
%!                            "display", "on", "varnames", {"seconds", "subject"});

%!demo
%!
%! # Balanced two-way ANOVA with interaction on the data from a study of popcorn
%! # brands and popper types, in Hogg and Ledolter (1987) Engineering Statistics.
%! # New York: MacMillan
%!
%! popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
%!            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
%! brands = {"Gourmet", "National", "Generic"; ...
%!           "Gourmet", "National", "Generic"; ...
%!           "Gourmet", "National", "Generic"; ...
%!           "Gourmet", "National", "Generic"; ...
%!           "Gourmet", "National", "Generic"; ...
%!           "Gourmet", "National", "Generic"};
%! popper = {"oil", "oil", "oil"; "oil", "oil", "oil"; "oil", "oil", "oil"; ...
%!           "air", "air", "air"; "air", "air", "air"; "air", "air", "air"};
%!
%! [P, ATAB, STATS] = anovan (popcorn(:), {brands(:), popper(:)}, ...
%!                            "display", "on", "model", "full", ...
%!                            "varnames", {"brands", "popper"});

%!demo
%!
%! # Unbalanced two-way ANOVA (2x2) on the data from a study on the effects of
%! # gender and having a college degree on salaries of company employees,
%! # in Maxwell, Delaney and Kelly (2018): Chapter 7, Table 15
%!
%! salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
%!           25 29 27 19 18 21 20 21 22 19]';
%! gender = {"f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"...
%!           "m" "m" "m" "m" "m" "m" "m" "m" "m" "m"}';
%! degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
%!
%! [P, ATAB, STATS] = anovan (salary, {gender, degree}, "model", "full", ...
%!                            "sstype", 3, "display", "on", "varnames", ...
%!                            {"gender", "degree"});

%!demo
%!
%! # Unbalanced two-way ANOVA (3x2) on the data from a study of the effect of
%! # adding sugar and/or milk on the tendency of coffee to make people babble,
%! # in from Navarro (2019): 16.10
%!
%! sugar = {"real" "fake" "fake" "real" "real" "real" "none" "none" "none" ...
%!          "fake" "fake" "fake" "real" "real" "real" "none" "none" "fake"}';
%! milk = {"yes" "no" "no" "yes" "yes" "no" "yes" "yes" "yes" ...
%!         "no" "no" "yes" "no" "no" "no" "no" "no" "yes"}';
%! babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
%!           5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
%!
%! [P, ATAB, STATS] = anovan (babble, {sugar, milk}, "model", "full",  ...
%!                            "sstype", 3, "display", "on", ...
%!                            "varnames", {"sugar", "milk"});

%!demo
%!
%! # Unbalanced three-way ANOVA (3x2x2) on the data from a study of the effects
%! # of three different drugs, biofeedback and diet on patient blood pressure,
%! # adapted* from Maxwell, Delaney and Kelly (2018): Chapter 8, Table 12
%! # * Missing values introduced to make the sample sizes unequal to test the
%! #   calculation of different types of sums-of-squares
%!
%! drug = {"X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" ...
%!         "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X";
%!         "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" ...
%!         "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y";
%!         "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" ...
%!         "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z"};
%! feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
%!             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
%!             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
%! diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
%!         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
%!         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
%! BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
%!       173 194 197 190 176 198 164 190 169 164 176 175;
%!       186 194 201 215 219 209 164 166 159 182 187 174 ...
%!       189 194 217 206 199 195 171 173 196 199 180 NaN;
%!       180 187 199 170 204 194 162 184 183 156 180 173 ...
%!       202 228 190 206 224 204 205 199 170 160 NaN NaN];
%!
%! [P, ATAB, STATS] = anovan (BP(:), {drug(:), feedback(:), diet(:)}, ...
%!                                    "model", "full", "sstype", 3, ...
%!                                    "display", "on", ...
%!                                    "varnames", {"drug", "feedback", "diet"});

%!demo
%!
%! # Balanced three-way ANOVA (2x2x2) with one of the factors being a blocking
%! # factor. The data is from a randomized block design study on the effects
%! # of antioxidant treatment on glutathione-S-transferase (GST) levels in
%! # different mouse strains, from Festing (2014), ILAR Journal, 55(3):427-476.
%! # Note that all interactions involving block were dropped from the full model
%! # by assigning block as a random factor (').
%!
%! measurement = [444 614 423 625 408  856 447 719 ...
%!                764 831 586 782 609 1002 606 766]';
%! strain= {"NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola", ...
%!          "NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola"}';
%! treatment={"C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T"}';
%! block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
%!
%! [P, ATAB, STATS] = anovan (measurement/10, {strain, treatment, block}, ...
%!                            "sstype", 2, "model", "full", "random", 3, ...
%!                            "display", "on", ...
%!                            "varnames", {"strain", "treatment", "block"});

%!demo
%!
%! # One-way ANCOVA on data from a study of the additive effects of species
%! # and temperature on chirpy pulses of crickets, from Stitch, The Worst Stats
%! # Text eveR
%!
%! pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
%!          98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
%!          60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
%! temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
%!         29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
%!         21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
%! species = {"ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" ...
%!            "ex" "ex" "ex" "niv" "niv" "niv" "niv" "niv" "niv" "niv" ...
%!            "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv"};
%!
%! [P, ATAB, STATS] = anovan (pulse, {species, temp}, "model", "linear", ...
%!                           "continuous", 2, "sstype", "h", "display", "on", ...
%!                           "varnames", {"species", "temp"});

%!demo
%!
%! # Factorial ANCOVA on data from a study of the effects of treatment and
%! # exercise on stress reduction score after adjusting for age. Data from R
%! # datarium package).
%!
%! score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
%!          97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
%!          81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
%!          84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
%!          100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
%!          91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
%! treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
%!              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
%!              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
%!              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
%!              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
%!              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
%! exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
%!             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
%!             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
%!             "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
%!             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
%!             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
%! age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
%!        58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
%!        75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
%!
%! [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, ...
%!                            "model", [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
%!                            "continuous", 3, "sstype", "h", "display", "on", ...
%!                            "varnames", {"treatment", "exercise", "age"});

%!demo
%!
%! # Unbalanced one-way ANOVA with custom, orthogonal contrasts. The statistics
%! # relating to the contrasts are shown in the table of model parameters, and
%! # can be retrieved from the STATS.coeffs output.
%!
%! dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
%!        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
%!        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
%!        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
%!        25.694 ]';
%! g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
%!      4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
%! C = [ 0.4001601  0.3333333  0.5  0.0
%!       0.4001601  0.3333333 -0.5  0.0
%!       0.4001601 -0.6666667  0.0  0.0
%!      -0.6002401  0.0000000  0.0  0.5
%!      -0.6002401  0.0000000  0.0 -0.5];
%!
%! [P,ATAB, STATS] = anovan (dv, g, "contrasts", C, "varnames", "score", ...
%!                          "alpha", 0.05, "display", "on");

%!demo
%!
%! # One-way ANOVA with the linear model fit by weighted least squares to
%! # account for heteroskedasticity. In this example, the variance appears
%! # proportional to the outcome, so weights have been estimated by initially
%! # fitting the model without weights and regressing the absolute residuals on
%! # the fitted values. Although this data could have been analysed by Welch's
%! # ANOVA test, the approach here can generalize to ANOVA models with more than
%! # one factor.
%!
%! g = [1, 1, 1, 1, 1, 1, 1, 1, ...
%!      2, 2, 2, 2, 2, 2, 2, 2, ...
%!      3, 3, 3, 3, 3, 3, 3, 3]';
%! y = [13, 16, 16,  7, 11,  5,  1,  9, ...
%!      10, 25, 66, 43, 47, 56,  6, 39, ...
%!      11, 39, 26, 35, 25, 14, 24, 17]';
%!
%! [P,ATAB,STATS] = anovan(y, g, "display", "off");
%! fitted = STATS.X * STATS.coeffs(:,1); # fitted values
%! b = polyfit (fitted, abs (STATS.resid), 1);
%! v = polyval (b, fitted);  # Variance as a function of the fitted values
%! figure("Name", "Regression of the absolute residuals on the fitted values");
%! plot (fitted, abs (STATS.resid),'ob');hold on; plot(fitted,v,'-r'); hold off;
%! xlabel("Fitted values"); ylabel("Absolute residuals");
%!
%! [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1);


## Test 1 for anovan example 1
## Test compares anovan to results from MATLAB's anovan and ttest2 functions
%!test
%! score = [54 23 45 54 45 43 34 65 77 46 65]';
%! gender = {'male' 'male' 'male' 'male' 'male' 'female' 'female' 'female' ...
%!           'female' 'female' 'female'}';
%!
%! [P, T, STATS] = anovan (score,gender,'display','off');
%! assert (P(1), 0.2612876773271042, 1e-09);              # compared to p calculated by MATLAB anovan
%! assert (sqrt(T{2,6}), abs(1.198608733288208), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
%! assert (P(1), 0.2612876773271047, 1e-09);              # compared to p calculated by MATLAB ttest2
%! assert (sqrt(T{2,6}), abs(-1.198608733288208), 1e-09); # compared to abs(t) calculated by MATLAB ttest2

## Test 2 for anovan example 2
## Test compares anovan to results from MATLAB's anovan and ttest functions
%!test
%! score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
%! treatment = {'before' 'after'; 'before' 'after'; 'before' 'after';
%!              'before' 'after'; 'before' 'after'}';
%! subject = {'GS' 'GS'; 'JM' 'JM'; 'HM' 'HM'; 'JW' 'JW'; 'PS' 'PS'}';
%!
%! [P, ATAB, STATS] = anovan (score(:),{treatment(:),subject(:)},'display','off','sstype',2);
%! assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB anovan
%! assert (sqrt(ATAB{2,6}), abs(4.00941576558195), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
%! assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB ttest2
%! assert (sqrt(ATAB{2,6}), abs(-4.00941576558195), 1e-09); # compared to abs(t) calculated by MATLAB ttest2

## Test 3 for anovan example 3
## Test compares anovan to results from MATLAB's anovan and anova1 functions
%!test
%! strength = [82 86 79 83 84 85 86 87 74 82 ...
%!            78 75 76 77 79 79 77 78 82 79]';
%! alloy = {'st','st','st','st','st','st','st','st', ...
%!          'al1','al1','al1','al1','al1','al1', ...
%!          'al2','al2','al2','al2','al2','al2'}';
%!
%! [P, ATAB, STATS] = anovan (strength,{alloy},'display','off');
%! assert (P(1), 0.000152643638830491, 1e-09);
%! assert (ATAB{2,6}, 15.4, 1e-09);

## Test 4 for anovan example 4
## Test compares anovan to results from MATLAB's anovan function
%!test
%! words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
%!          15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
%! subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
%!             6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
%! seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
%!            1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
%!
%! [P, ATAB, STATS] = anovan (words(:),{seconds(:),subject(:)},'model','full','random',2,'sstype',2,'display','off');
%! assert (P(1), 1.51865926758752e-07, 1e-09);
%! assert (ATAB{2,2}, 52.2666666666667, 1e-09);
%! assert (ATAB{3,2}, 942.533333333333, 1e-09);
%! assert (ATAB{4,2}, 11.0666666666667, 1e-09);

## Test 5 for anovan example 5
## Test compares anovan to results from MATLAB's anovan function
%!test
%! popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
%!            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
%! brands = {'Gourmet', 'National', 'Generic'; ...
%!           'Gourmet', 'National', 'Generic'; ...
%!           'Gourmet', 'National', 'Generic'; ...
%!           'Gourmet', 'National', 'Generic'; ...
%!           'Gourmet', 'National', 'Generic'; ...
%!           'Gourmet', 'National', 'Generic'};
%! popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
%!           'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
%!
%! [P, ATAB, STATS] = anovan (popcorn(:),{brands(:),popper(:)},'display','off','model','full');
%! assert (P(1), 7.67895738278171e-07, 1e-09);
%! assert (P(2), 0.000100373896304998, 1e-09);
%! assert (P(3), 0.746215396636649, 1e-09);
%! assert (ATAB{2,6}, 56.7, 1e-09);
%! assert (ATAB{3,6}, 32.4, 1e-09);
%! assert (ATAB{4,6}, 0.29999999999997, 1e-09);

## Test 6 for anovan example 6
## Test compares anovan to results from MATLAB's anovan function
%!test
%! salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
%!           25 29 27 19 18 21 20 21 22 19]';
%! gender = {'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f'...
%!           'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm'}';
%! degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
%!
%! [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',1,'display','off');
%! assert (P(1), 0.747462549227232, 1e-09);
%! assert (P(2), 1.03809316857694e-08, 1e-09);
%! assert (P(3), 0.523689833702691, 1e-09);
%! assert (ATAB{2,2}, 0.296969696969699, 1e-09);
%! assert (ATAB{3,2}, 272.391841491841, 1e-09);
%! assert (ATAB{4,2}, 1.17482517482512, 1e-09);
%! assert (ATAB{5,2}, 50.0000000000001, 1e-09);
%! [P, ATAB, STATS] = anovan (salary,{degree,gender},'model','full','sstype',1,'display','off');
%! assert (P(1), 2.53445097305047e-08, 1e-09);
%! assert (P(2), 0.00388133678528749, 1e-09);
%! assert (P(3), 0.523689833702671, 1e-09);
%! assert (ATAB{2,2}, 242.227272727273, 1e-09);
%! assert (ATAB{3,2}, 30.4615384615384, 1e-09);
%! assert (ATAB{4,2}, 1.17482517482523, 1e-09);
%! assert (ATAB{5,2}, 50.0000000000001, 1e-09);
%! [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',2,'display','off');
%! assert (P(1), 0.00388133678528743, 1e-09);
%! assert (P(2), 1.03809316857694e-08, 1e-09);
%! assert (P(3), 0.523689833702691, 1e-09);
%! assert (ATAB{2,2}, 30.4615384615385, 1e-09);
%! assert (ATAB{3,2}, 272.391841491841, 1e-09);
%! assert (ATAB{4,2}, 1.17482517482512, 1e-09);
%! assert (ATAB{5,2}, 50.0000000000001, 1e-09);
%! [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',3,'display','off');
%! assert (P(1), 0.00442898146583742, 1e-09);
%! assert (P(2), 1.30634252053587e-08, 1e-09);
%! assert (P(3), 0.523689833702691, 1e-09);
%! assert (ATAB{2,2}, 29.3706293706294, 1e-09);
%! assert (ATAB{3,2}, 264.335664335664, 1e-09);
%! assert (ATAB{4,2}, 1.17482517482512, 1e-09);
%! assert (ATAB{5,2}, 50.0000000000001, 1e-09);

## Test 7 for anovan example 7
## Test compares anovan to results from MATLAB's anovan function
%!test
%! sugar = {'real' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'none' ...
%!          'fake' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'fake'}';
%! milk = {'yes' 'no' 'no' 'yes' 'yes' 'no' 'yes' 'yes' 'yes' ...
%!         'no' 'no' 'yes' 'no' 'no' 'no' 'no' 'no' 'yes'}';
%! babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
%!           5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
%!
%! [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',1,'display','off');
%! assert (P(1), 0.0108632139833963, 1e-09);
%! assert (P(2), 0.0810606976703546, 1e-09);
%! assert (P(3), 0.00175433329935627, 1e-09);
%! assert (ATAB{2,2}, 3.55752380952381, 1e-09);
%! assert (ATAB{3,2}, 0.956108477471702, 1e-09);
%! assert (ATAB{4,2}, 5.94386771300448, 1e-09);
%! assert (ATAB{5,2}, 3.1625, 1e-09);
%! [P, ATAB, STATS] = anovan (babble,{milk,sugar},'model','full','sstype',1,'display','off');
%! assert (P(1), 0.0373333189297505, 1e-09);
%! assert (P(2), 0.017075098787169, 1e-09);
%! assert (P(3), 0.00175433329935627, 1e-09);
%! assert (ATAB{2,2}, 1.444, 1e-09);
%! assert (ATAB{3,2}, 3.06963228699552, 1e-09);
%! assert (ATAB{4,2}, 5.94386771300448, 1e-09);
%! assert (ATAB{5,2}, 3.1625, 1e-09);
%! [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',2,'display','off');
%! assert (P(1), 0.017075098787169, 1e-09);
%! assert (P(2), 0.0810606976703546, 1e-09);
%! assert (P(3), 0.00175433329935627, 1e-09);
%! assert (ATAB{2,2}, 3.06963228699552, 1e-09);
%! assert (ATAB{3,2}, 0.956108477471702, 1e-09);
%! assert (ATAB{4,2}, 5.94386771300448, 1e-09);
%! assert (ATAB{5,2}, 3.1625,  1e-09);
%! [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',3,'display','off');
%! assert (P(1), 0.0454263063473954, 1e-09);
%! assert (P(2), 0.0746719907091438, 1e-09);
%! assert (P(3), 0.00175433329935627, 1e-09);
%! assert (ATAB{2,2}, 2.13184977578476, 1e-09);
%! assert (ATAB{3,2}, 1.00413461538462, 1e-09);
%! assert (ATAB{4,2}, 5.94386771300448, 1e-09);
%! assert (ATAB{5,2}, 3.1625, 1e-09);

## Test 8 for anovan example 8
## Test compares anovan to results from MATLAB's anovan function
%!test
%! drug = {'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' ...
%!         'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X';
%!         'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' ...
%!         'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y';
%!         'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' ...
%!         'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z'};
%! feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
%!             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
%!             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
%! diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
%!         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
%!         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
%! BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
%!       173 194 197 190 176 198 164 190 169 164 176 175;
%!       186 194 201 215 219 209 164 166 159 182 187 174 ...
%!       189 194 217 206 199 195 171 173 196 199 180 NaN;
%!       180 187 199 170 204 194 162 184 183 156 180 173 ...
%!       202 228 190 206 224 204 205 199 170 160 NaN NaN];
%!
%! [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 1,'display','off');
%! assert (P(1), 7.02561843825325e-05, 1e-09);
%! assert (P(2), 0.000425806013389362, 1e-09);
%! assert (P(3), 6.16780773446401e-07, 1e-09);
%! assert (P(4), 0.261347622678438, 1e-09);
%! assert (P(5), 0.0542278432357043, 1e-09);
%! assert (P(6), 0.590353225626655, 1e-09);
%! assert (P(7), 0.0861628249564267, 1e-09);
%! assert (ATAB{2,2}, 3614.70355731226, 1e-09);
%! assert (ATAB{3,2}, 2227.46639771024, 1e-09);
%! assert (ATAB{4,2}, 5008.25614451819, 1e-09);
%! assert (ATAB{5,2}, 437.066007908781, 1e-09);
%! assert (ATAB{6,2}, 976.180770397332, 1e-09);
%! assert (ATAB{7,2}, 46.616653365254, 1e-09);
%! assert (ATAB{8,2}, 814.345251396648, 1e-09);
%! assert (ATAB{9,2}, 9065.8,  1e-09);
%! [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype',2,'display','off');
%! assert (P(1), 9.4879638470754e-05, 1e-09);
%! assert (P(2), 0.00124177666315809, 1e-09);
%! assert (P(3), 6.86162012732911e-07, 1e-09);
%! assert (P(4), 0.260856132341256, 1e-09);
%! assert (P(5), 0.0523758623892078, 1e-09);
%! assert (P(6), 0.590353225626655, 1e-09);
%! assert (P(7), 0.0861628249564267, 1e-09);
%! assert (ATAB{2,2}, 3481.72176560122, 1e-09);
%! assert (ATAB{3,2}, 1837.08812970469, 1e-09);
%! assert (ATAB{4,2}, 4957.20277938622, 1e-09);
%! assert (ATAB{5,2}, 437.693674777847, 1e-09);
%! assert (ATAB{6,2}, 988.431929811402, 1e-09);
%! assert (ATAB{7,2}, 46.616653365254, 1e-09);
%! assert (ATAB{8,2}, 814.345251396648, 1e-09);
%! assert (ATAB{9,2}, 9065.8,  1e-09);
%! [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 3,'display','off');
%! assert (P(1), 0.000106518678028207, 1e-09);
%! assert (P(2), 0.00125371366571508, 1e-09);
%! assert (P(3), 5.30813260778464e-07, 1e-09);
%! assert (P(4), 0.308353667232981, 1e-09);
%! assert (P(5), 0.0562901327343161, 1e-09);
%! assert (P(6), 0.599091042141092, 1e-09);
%! assert (P(7), 0.0861628249564267, 1e-09);
%! assert (ATAB{2,2}, 3430.88156424581, 1e-09);
%! assert (ATAB{3,2}, 1833.68031496063, 1e-09);
%! assert (ATAB{4,2}, 5080.48346456693, 1e-09);
%! assert (ATAB{5,2}, 382.07709497207, 1e-09);
%! assert (ATAB{6,2}, 963.037988826813, 1e-09);
%! assert (ATAB{7,2}, 44.4519685039322, 1e-09);
%! assert (ATAB{8,2}, 814.345251396648, 1e-09);
%! assert (ATAB{9,2}, 9065.8, 1e-09);

## Test 9 for anovan example 9
## Test compares anovan to results from MATLAB's anovan function
%!test
%! measurement = [444 614 423 625 408  856 447 719 ...
%!                764 831 586 782 609 1002 606 766]';
%! strain= {'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola', ...
%!          'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola'}';
%! treatment={'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T'}';
%! block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
%!
%! [P, ATAB, STATS] = anovan (measurement/10,{strain,treatment,block},'model','full','random',3,'display','off');
%! assert (P(1), 0.0914352969909372, 1e-09);
%! assert (P(2), 5.04077373924908e-05, 1e-09);
%! assert (P(4), 0.0283196918836667, 1e-09);
%! assert (ATAB{2,2}, 286.132500000002, 1e-09);
%! assert (ATAB{3,2}, 2275.29, 1e-09);
%! assert (ATAB{4,2}, 1242.5625, 1e-09);
%! assert (ATAB{5,2}, 495.905000000001, 1e-09);
%! assert (ATAB{6,2}, 207.007499999999, 1e-09);

## Test 10 for anovan example 10
## Test compares anovan to results from MATLAB's anovan function
%!test
%! pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
%!          98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
%!          60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
%! temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
%!         29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
%!         21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
%! species = {'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' ...
%!            'ex' 'ex' 'ex' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' ...
%!            'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv'};
%!
%! [P, ATAB, STATS] = anovan (pulse,{species,temp},'model','linear','continuous',2,'sstype','h','display','off');
%! assert (P(1), 6.27153318786007e-14, 1e-09);
%! assert (P(2), 2.48773241196644e-25, 1e-09);
%! assert (ATAB{2,2}, 598.003953318404, 1e-09);
%! assert (ATAB{3,2}, 4376.08256843712, 1e-09);
%! assert (ATAB{4,2}, 89.3498685376726, 1e-09);
%! assert (ATAB{2,6}, 187.399388123951, 1e-09);
%! assert (ATAB{3,6}, 1371.35413763454, 1e-09);

## Test 11 for anovan example 11
## Test compares anovan to results from MATLAB's anovan function
%!test
%! score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
%!          97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
%!          81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
%!          84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
%!          100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
%!          91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
%! treatment = {'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
%!              'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
%!              'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
%!              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
%!              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
%!              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'}';
%! exercise = {'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
%!             'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
%!             'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  ...
%!             'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
%!             'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
%!             'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'}';
%! age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
%!        58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
%!        75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
%!
%! [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model','full','continuous',3,'sstype','h','display','off');
%! assert (P(5), 0.9245630968248468, 1e-09);
%! assert (P(6), 0.791115159521822, 1e-09);
%! assert (P(7), 0.9296668751457956, 1e-09);
%! [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model',[1 0 0; 0 1 0; 0 0 1; 1 1 0],'continuous',3,'sstype','h','display','off');
%! assert (P(1), 0.00158132928938933, 1e-09);
%! assert (P(2), 2.12537505039986e-07, 1e-09);
%! assert (P(3), 0.00390292555160047, 1e-09);
%! assert (P(4), 0.0164086580775543, 1e-09);
%! assert (ATAB{2,6}, 11.0956027650549, 1e-09);
%! assert (ATAB{3,6}, 20.8195665467178, 1e-09);
%! assert (ATAB{4,6}, 9.10966630720186, 1e-09);
%! assert (ATAB{5,6}, 4.4457923698584, 1e-09);

## Test 12 for anovan example 12
## Test compares anovan regression coefficients to R:
## https://www.uvm.edu/~statdhtx/StatPages/Unequal-ns/Unequal_n%27s_contrasts.html
%!test
%! dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
%!        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
%!        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
%!        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
%!        25.694 ]';
%! g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
%! C = [ 0.4001601  0.3333333  0.5  0.0
%!       0.4001601  0.3333333 -0.5  0.0
%!       0.4001601 -0.6666667  0.0  0.0
%!      -0.6002401  0.0000000  0.0  0.5
%!      -0.6002401  0.0000000  0.0 -0.5];
%!
%! [P,ATAB,STATS] = anovan (dv,g,'contrasts',{C},'display','off');
%! assert (STATS.coeffs(1,1), 19.4001, 1e-04);
%! assert (STATS.coeffs(2,1), -9.3297, 1e-04);
%! assert (STATS.coeffs(3,1), -5.0000, 1e-04);
%! assert (STATS.coeffs(4,1), -8.0000, 1e-04);
%! assert (STATS.coeffs(5,1), -8.0000, 1e-04);
%! assert (STATS.coeffs(1,2), 0.4831, 1e-04);
%! assert (STATS.coeffs(2,2), 0.9694, 1e-04);
%! assert (STATS.coeffs(3,2), 1.3073, 1e-04);
%! assert (STATS.coeffs(4,2), 1.6411, 1e-04);
%! assert (STATS.coeffs(5,2), 1.4507, 1e-04);
%! assert (STATS.coeffs(1,5), 40.161, 1e-03);
%! assert (STATS.coeffs(2,5), -9.624, 1e-03);
%! assert (STATS.coeffs(3,5), -3.825, 1e-03);
%! assert (STATS.coeffs(4,5), -4.875, 1e-03);
%! assert (STATS.coeffs(5,5), -5.515, 1e-03);
%! assert (STATS.coeffs(2,6), 5.74e-11, 1e-12);
%! assert (STATS.coeffs(3,6), 0.000572, 1e-06);
%! assert (STATS.coeffs(4,6), 2.86e-05, 1e-07);
%! assert (STATS.coeffs(5,6), 4.44e-06, 1e-08);
