### Function Reference: `anova2`

statistics: p = anova2 (x, reps)
statistics: p = anova2 (x, reps, displayopt)
statistics: p = anova2 (x, reps, displayopt, model)
statistics: [p, atab] = anova2 (…)
statistics: [p, atab, stats] = anova2 (…)

Performs two-way factorial (crossed) or a nested analysis of variance (ANOVA) for balanced designs. For unbalanced factorial designs, diagnostic plots and/or planned contrasts, use `anovan` instead.

`anova2` requires two input arguments with an optional third and fourth:

- x contains the data and it must be a matrix of at least two columns and two rows.
- reps is the number of replicates for each combination of factor groups.
- displayopt is an optional parameter for displaying the ANOVA table, when it is ’on’ (default) and suppressing the display when it is ’off’.
- model is an optional parameter to specify the model type as either:
  - "interaction" or "full" (default): compute both main effects and their interaction
  - "linear": compute both main effects without an interaction. When reps > 1 the test is suitable for a balanced randomized block design. When reps == 1, the test becomes a One-way Repeated Measures (RM)-ANOVA with Greenhouse-Geisser correction to the column factor degrees of freedom to make the test robust to violations of sphericity
  - "nested": treat the row factor as nested within columns. Note that the row factor is considered a random factor in the calculation of the statistics.

`anova2` returns up to three output arguments:

- p is the p-value of the null hypothesis that all group means are equal.
- atab is a cell array containing the results in a standard ANOVA table.
- stats is a structure containing statistics useful for performing a multiple comparison of means with the MULTCOMPARE function.

If anova2 is called without any output arguments, then it prints the results in a one-way ANOVA table to the standard output as if displayopt is ’on’.

Examples:

     load popcorn;
     anova2 (popcorn, 3);

     [p, anovatab, stats] = anova2 (popcorn, 3, "off");
     disp (p);

See also: anova1, anovan, multcompare

Source Code: anova2

### Example: 1

     # Factorial (Crossed) Two-way ANOVA with Interaction

     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];

     [p, atab, stats] = anova2(popcorn, 3, "on");

                          ANOVA Table

    Source             SS      df        MS       F      Prob>F
    -----------------------------------------------------------
    Columns         15.7500     2     7.8750    56.70    0.0000
    Rows             4.5000     1     4.5000    32.40    0.0001
    Interaction      0.0833     2     0.0417     0.30    0.7462
    Error            1.6667    12     0.1389
    Total           22.0000    17

### Example: 2

     # One-way Repeated Measures ANOVA (Rows are a crossed random factor)

     data = [54, 43, 78, 111;
             23, 34, 37, 41;
             45, 65, 99, 78;
             31, 33, 36, 35;
             15, 25, 30, 26];

     [p, atab, stats] = anova2 (data, 1, "on", "linear");

                          ANOVA Table

    Source             SS      df        MS       F      Prob>F
    -----------------------------------------------------------
    Columns       2174.9500     3   724.9833     3.62    0.0873
    Rows          8371.7000     4  2092.9250    10.45    0.0007
    Error         2404.3000    12   200.3583
    Total        12950.9500    19

    Note: Greenhouse-Geisser's correction was applied to the
    degrees of freedom for the Column factor: F(1.74,6.95)

### Example: 3

     # Balanced Nested One-way ANOVA (Rows are a nested random factor)

     data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
             6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
             5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
             6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];

     [p, atab, stats] = anova2 (data, 4, "on", "nested");

                          ANOVA Table

    Source             SS      df        MS       F      Prob>F
    -----------------------------------------------------------
    Columns        745.3603     2   372.6802     4.02    0.1416
    Rows           278.0185     3    92.6728     9.26    0.0006
    Error          180.1804    18    10.0100
    Total         1203.5592    23

    Note: Rows are a random factor nested within the columns.
    The Column F statistic uses the Row MS instead of the MSE.