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Multiple comparison test

`c = multcompare(stats)`

`c = multcompare(stats,Name,Value)`

```
[c,m] =
multcompare(___)
```

```
[c,m,h]
= multcompare(___)
```

```
[c,m,h,gnames]
= multcompare(___)
```

returns
a matrix `c`

= multcompare(`stats`

)`c`

of the pairwise comparison results
from a multiple comparison test using the information contained in
the `stats`

structure. `multcompare`

also
displays an interactive graph of the estimates and comparison intervals.
Each group mean is represented by a symbol, and the interval is represented
by a line extending out from the symbol. Two group means are significantly
different if their intervals are disjoint; they are not significantly
different if their intervals overlap. If you use your mouse to select
any group, then the graph will highlight all other groups that are
significantly different, if any.

returns
a matrix of pairwise comparison results, `c`

= multcompare(`stats`

,`Name,Value`

)`c`

, using
additional options specified by one or more `Name,Value`

pair
arguments. For example, you can specify the confidence interval, or
the type of critical value to use in the multiple comparison.

[1] Hochberg, Y., and A. C. Tamhane. *Multiple
Comparison Procedures*. Hoboken, NJ: John Wiley & Sons,
1987.

[2] Milliken, G. A., and D. E. Johnson. *Analysis
of Messy Data, Volume I: Designed Experiments*. Boca Raton,
FL: Chapman & Hall/CRC Press, 1992.

[3] Searle, S. R., F. M. Speed, and G. A.
Milliken. “Population marginal means in the linear model:
an alternative to least-squares means.” *American
Statistician*. 1980, pp. 216–221.

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