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# anova

Clase: CompactLinearModel

Analysis of variance for linear model

## Sintaxis

```tbl = anova(mdl) tbl = anova(mdl,anovatype) tbl = anova(mdl,anovatype,sstype) ```

## Description

`tbl = anova(mdl)` returns a table with summary ANOVA statistics.

`tbl = anova(mdl,anovatype)` returns ANOVA statistics of the chosen type.

`tbl = anova(mdl,anovatype,sstype)` computes ANOVA statistics using the chosen type of sum of squares.

## Argumentos de entrada

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Linear model object, specified as a full `LinearModel` object constructed using `fitlm` or `stepwiselm`, or a compacted `CompactLinearModel` object constructed using `compact`.

ANOVA type, specified as one of the following:

• `'component'``tbl` displays a ‘components’ ANOVA table, with sums of squares and F tests attributable to each term in the model except the constant term.

• `'summary'``tbl` displays a summary ANOVA table with an F test for the model as a whole.

• If there are both linear and higher-order terms, there is also an F test for the higher-order terms as a group.

• If `mdl` is a full `LinearModel` object and the sample data contains replications (multiple observations sharing the same predictor values), there is also an F test for lack-of-fit computed by decomposing the residual sum of squares into a sum of squares for the replicated observations and the remaining sum of squares. If `mdl` is a compacted `CompactLinearModel` object, then the ANOVA table presents, but does not decompose, the residual sum of squares.

Sum of squares type, specified as one of the following:

ValueDescription
`1`Type I sum of squares. The reduction in residual sum of squares obtained by adding that term to a fit that already includes the terms listed before it.
`2`Type II sum of squares. The reduction in residual sum of squares obtained by adding that term to a model consisting of all other terms that do not contain the term in question
`3`Type III sum of squares. The reduction in residual sum of squares obtained by adding that term to a model containing all other terms, but with their effects constrained to obey the usual “sigma restrictions” that make models estimable.
`'h'`Hierarchical model. Similar to Type II, but uses both continuous and categorical factors to determine the hierarchy of terms.

For Type III sum of squares:

• If `mdl` is a `CompactLinearModel` object and the model is non-hierarchical, `anova` errors.

• If `mdl` is a full `LinearModel` object and the model is non-hierarchical, then `anova` refits the model using effects coding whenever it needs to compute Type III sums of squares.

• If `mdl` is either a `CompactLinearModel` or a `LinearModel` object and the model is hierarchical, `anova` computes the results without refitting the model.

`sstype` only applies if `anovatype` is `'component'`.

For more information about sum of squares types, see the `sstype` argument on the `anovan` function reference page.

Tipos de datos: `char` | `string` | `single` | `double`

## Output Arguments

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ANOVA summary statistics table, returned as table

The contents of `tbl` depends on the ANOVA type specified in `anovatype`.

• If `anovatype` is `'component'`, then `tbl` contains:

• Sum of squares

• Degrees of freedom

• Mean squares

• F statistic

• p-value

• Formula used for model

• If `anovatype` is `'summary'`, then `tbl` contains:

• Total Sum of Squares

• Model Sum of Squares

• Linear Sum of Squares (present if model has powers or interactions)

• Nonlinear Sum of Squares (present if model has powers or interactions)

• Residual Sum of Squares

• Lack-of-fit Sum of Squares (present if model has replicates)

• Pure error Sum of Squares (present if model has replicates)

## Ejemplos

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Create a component ANOVA table from a model of the `hospital` data.

Load the `hospital` data and make a model of the blood pressure as a function of age and gender.

```load hospital tbl = table(hospital.Age,hospital.Sex,hospital.BloodPressure(:,2), ... 'VariableNames',{'Age','Sex','BloodPressure'}); tbl.Sex = categorical(tbl.Sex); mdl = fitlm(tbl,'BloodPressure ~ Sex + Age^2');```

Create an ANOVA table.

`tbl = anova(mdl)`
```tbl=4×5 table SumSq DF MeanSq F pValue ______ __ ______ _______ ________ Age 18.705 1 18.705 0.40055 0.52831 Sex 222.09 1 222.09 4.7558 0.031643 Age^2 30.934 1 30.934 0.66242 0.41772 Error 4483.1 96 46.699 ```

Create a summary ANOVA table from a model of the `carsmall` data.

Load the `carsmall` data and make a model of the MPG as a function of weight and model year.

```load carsmall cars = table(MPG,Weight); cars.Year = ordinal(Model_Year); mdl = fitlm(cars,'MPG ~ Year + Weight^2');```

Create a summary ANOVA table.

`tbl = anova(mdl,'summary')`
```tbl=7×5 table SumSq DF MeanSq F pValue ______ __ ______ ______ __________ Total 6005.3 93 64.573 Model 5317 4 1329.3 171.88 5.5208e-41 . Linear 5240.3 3 1746.8 225.87 1.7302e-41 . Nonlinear 76.688 1 76.688 9.9164 0.0022303 Residual 688.27 89 7.7334 . Lack of fit 663.77 86 7.7183 0.9451 0.62874 . Pure error 24.5 3 8.1667 ```

The summary ANOVA table shows tests for groups of terms. The nonlinear group consists of just the `Weight^2` term, so it has the same -value as that term in the component ANOVA table. The statistic comparing the residual sum of squares to a "pure error" estimate from replicated observations shows no evidence of lack of fit.

## Alternatives

More complete ANOVA statistics are available in the `anova1`, `anova2`, and `anovan` functions.