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# ranova

Clase: RepeatedMeasuresModel

Repeated measures analysis of variance

## Sintaxis

``ranovatbl = ranova(rm)``
``ranovatbl = ranova(rm,'WithinModel',WM)``
``````[ranovatbl,A,C,D] = ranova(___)``````

## Description

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````ranovatbl = ranova(rm)` returns the results of repeated measures analysis of variance for a repeated measures model `rm` in table `ranovatbl`.```

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````ranovatbl = ranova(rm,'WithinModel',WM)` returns the results of repeated measures analysis of variance using the responses specified by the within-subject model `WM`.```

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``````[ranovatbl,A,C,D] = ranova(___)``` also returns arrays `A`, `C`, and `D` for the hypotheses tests of the form ```A*B*C = D```, where `D` is zero.```

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Repeated measures model, returned as a `RepeatedMeasuresModel` object.

For properties and methods of this object, see `RepeatedMeasuresModel`.

Model specifying the responses, specified as one of the following:

• `'separatemeans'` — Compute a separate mean for each group.

• `C`r-by-nc contrast matrix specifying the nc contrasts among the r repeated measures. If Y represents a matrix of repeated measures, `ranova` tests the hypothesis that the means of Y*C are zero.

• A character vector or string scalar that defines a model specification in the within-subject factors. You can define the model based on the rules for the `terms` in the `modelspec` argument of `fitrm`. Also see Model Specification for Repeated Measures Models.

For example, if there are three within-subject factors `w1`, `w2`, and `w3`, then you can specify a model for the within-subject factors as follows.

Ejemplo: `'WithinModel','w1+w2+w2*w3'`

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

## Output Arguments

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Results of repeated measures anova, returned as a `table`.

`ranovatbl` includes a term representing all differences across the within-subjects factors. This term has either the name of the within-subjects factor if specified while fitting the model, or the name `Time` if the name of the within-subjects factor is not specified while fitting the model or there are more than one within-subjects factors. `ranovatbl` also includes all interactions between the terms in the within-subject model and all between-subject model terms. It contains the following columns.

Column NameDefinition
`SumSq`Sum of squares.
`DF`Degrees of freedom.
`MeanSq`Mean squared error.
`F`F-statistic.
`pValue`p-value for the corresponding F-statistic. A small p-value indicates significant term effect.
`pValueGG`p-value with Greenhouse-Geisser adjustment.
`pValueHF`p-value with Huynh-Feldt adjustment.
`pValueLB`p-value with Lower bound adjustment.

The last three p-values are the adjusted p-values for use when the compound symmetry assumption is not satisfied. For details, see Compound Symmetry Assumption and Epsilon Corrections. The `mauchy` method tests for sphericity (hence, compound symmetry) and `epsilon` method returns the epsilon adjustment values.

Specification based on the between-subjects model, returned as a matrix or a cell array. It permits the hypothesis on the elements within given columns of `B` (within time hypothesis). If `ranovatbl` contains multiple hypothesis tests, `A` might be a cell array.

Tipos de datos: `single` | `double` | `cell`

Specification based on the within-subjects model, returned as a matrix or a cell array. It permits the hypotheses on the elements within given rows of `B` (between time hypotheses). If `ranovatbl` contains multiple hypothesis tests, `C` might be a cell array.

Tipos de datos: `single` | `double` | `cell`

Hypothesis value, returned as 0.

## Ejemplos

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`load fisheriris`

The column vector `species` consists of iris flowers of three different species: setosa, versicolor, virginica. The double matrix `meas` consists of four types of measurements on the flowers: the length and width of sepals and petals in centimeters, respectively.

Store the data in a table array.

```t = table(species,meas(:,1),meas(:,2),meas(:,3),meas(:,4),... 'VariableNames',{'species','meas1','meas2','meas3','meas4'}); Meas = table([1 2 3 4]','VariableNames',{'Measurements'});```

Fit a repeated measures model, where the measurements are the responses and the species is the predictor variable.

`rm = fitrm(t,'meas1-meas4~species','WithinDesign',Meas);`

Perform repeated measures analysis of variance.

`ranovatbl = ranova(rm)`
```ranovatbl=3×8 table SumSq DF MeanSq F pValue pValueGG pValueHF pValueLB ______ ___ ________ ______ ___________ ___________ ___________ ___________ (Intercept):Measurements 1656.3 3 552.09 6873.3 0 9.4491e-279 2.9213e-283 2.5871e-125 species:Measurements 282.47 6 47.078 586.1 1.4271e-206 4.9313e-156 1.5406e-158 9.0151e-71 Error(Measurements) 35.423 441 0.080324 ```

There are four measurements, three types of species, and 150 observations. So, degrees of freedom for measurements is (4–1) = 3, for species-measurements interaction it is (4–1)*(3–1) = 6, and for error it is (150–4)*(3–1) = 441. `ranova` computes the last three -values using Greenhouse-Geisser, Huynh-Feldt, and Lower bound corrections, respectively. You can check the compound symmetry (sphericity) assumption using the `mauchly` method, and display the epsilon corrections using the `epsilon` method.

`load(fullfile(matlabroot,'examples','stats','longitudinalData.mat'));`

The matrix `Y` contains response data for 16 individuals. The response is the blood level of a drug measured at five time points (time = 0, 2, 4, 6, and 8). Each row of `Y` corresponds to an individual, and each column corresponds to a time point. The first eight subjects are female, and the second eight subjects are male. This is simulated data.

Define a variable that stores gender information.

`Gender = ['F' 'F' 'F' 'F' 'F' 'F' 'F' 'F' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M']';`

Store the data in a proper table array format to do repeated measures analysis.

```t = table(Gender,Y(:,1),Y(:,2),Y(:,3),Y(:,4),Y(:,5),... 'VariableNames',{'Gender','t0','t2','t4','t6','t8'});```

Define the within-subjects variable.

`Time = [0 2 4 6 8]';`

Fit a repeated measures model, where the blood levels are the responses and gender is the predictor variable.

`rm = fitrm(t,'t0-t8 ~ Gender','WithinDesign',Time);`

Perform repeated measures analysis of variance.

`ranovatbl = ranova(rm)`
```ranovatbl=3×8 table SumSq DF MeanSq F pValue pValueGG pValueHF pValueLB ______ __ ______ _______ __________ __________ __________ __________ (Intercept):Time 881.7 4 220.43 37.539 3.0348e-15 4.7325e-09 2.4439e-10 2.6198e-05 Gender:Time 17.65 4 4.4125 0.75146 0.56126 0.4877 0.50707 0.40063 Error(Time) 328.83 56 5.872 ```

There are 5 time points, 2 genders, and 16 observations. So, the degrees of freedom for time is (5–1) = 4, for gender-time interaction it is (5–1)*(2–1) = 4, and for error it is (16–2)*(5–1) = 56. The small -value of 2.6198e–05 indicates that there is a significant effect of time on blood pressure. The -value of 0.40063 indicates that there is no significant gender-time interaction.

`load repeatedmeas`

The table `between` includes the between-subject variables age, IQ, group, gender, and eight repeated measures `y1` through `y8` as responses. The table within includes the within-subject variables `w1` and `w2`. This is simulated data.

Fit a repeated measures model, where the repeated measures `y1` through `y8` are the responses, and age, IQ, group, gender, and the group-gender interaction are the predictor variables. Also specify the within-subject design matrix.

`rm = fitrm(between,'y1-y8 ~ Group*Gender + Age + IQ','WithinDesign',within);`

Perform repeated measures analysis of variance.

`ranovatbl = ranova(rm)`
```ranovatbl=7×8 table SumSq DF MeanSq F pValue pValueGG pValueHF pValueLB ______ ___ ______ _______ _________ ________ _________ ________ (Intercept):Time 6645.2 7 949.31 2.2689 0.031674 0.071235 0.056257 0.14621 Age:Time 5824.3 7 832.05 1.9887 0.059978 0.10651 0.090128 0.17246 IQ:Time 5188.3 7 741.18 1.7715 0.096749 0.14492 0.12892 0.19683 Group:Time 15800 14 1128.6 2.6975 0.0014425 0.011884 0.0064346 0.089594 Gender:Time 4455.8 7 636.55 1.5214 0.16381 0.20533 0.19258 0.23042 Group:Gender:Time 4247.3 14 303.38 0.72511 0.74677 0.663 0.69184 0.49549 Error(Time) 64433 154 418.39 ```

Specify the model for the within-subject factors. Also display the matrices used in the hypothesis test.

`[ranovatbl,A,C,D] = ranova(rm,'WithinModel','w1+w2')`
```ranovatbl=21×8 table SumSq DF MeanSq F pValue pValueGG pValueHF pValueLB ______ __ ______ ________ _________ _________ _________ _________ (Intercept) 3141.7 1 3141.7 2.5034 0.12787 0.12787 0.12787 0.12787 Age 537.48 1 537.48 0.42828 0.51962 0.51962 0.51962 0.51962 IQ 2975.9 1 2975.9 2.3712 0.13785 0.13785 0.13785 0.13785 Group 20836 2 10418 8.3012 0.0020601 0.0020601 0.0020601 0.0020601 Gender 3036.3 1 3036.3 2.4194 0.13411 0.13411 0.13411 0.13411 Group:Gender 211.8 2 105.9 0.084385 0.91937 0.91937 0.91937 0.91937 Error 27609 22 1255 1 0.5 0.5 0.5 0.5 (Intercept):w1 146.75 1 146.75 0.23326 0.63389 0.63389 0.63389 0.63389 Age:w1 942.02 1 942.02 1.4974 0.23402 0.23402 0.23402 0.23402 IQ:w1 11.563 1 11.563 0.01838 0.89339 0.89339 0.89339 0.89339 Group:w1 4481.9 2 2240.9 3.562 0.045697 0.045697 0.045697 0.045697 Gender:w1 270.65 1 270.65 0.4302 0.51869 0.51869 0.51869 0.51869 Group:Gender:w1 240.37 2 120.19 0.19104 0.82746 0.82746 0.82746 0.82746 Error(w1) 13841 22 629.12 1 0.5 0.5 0.5 0.5 (Intercept):w2 3663.8 3 1221.3 3.8381 0.013513 0.020339 0.01575 0.062894 Age:w2 1199.9 3 399.95 1.2569 0.2964 0.29645 0.29662 0.27432 ⋮ ```
```A = 6x1 cell array {1x8 double} {1x8 double} {1x8 double} {2x8 double} {1x8 double} {2x8 double} ```
```C = 1x3 cell array {8x1 double} {8x1 double} {8x3 double} ```
```D = 0 ```

Display the contents of `A`.

`[A{1};A{2};A{3};A{4};A{5};A{6}]`
```ans = 8×8 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 ```

Display the contents of `C`.

`[C{1} C{2} C{3}]`
```ans = 8×5 1 1 1 0 0 1 1 0 1 0 1 1 0 0 1 1 1 -1 -1 -1 1 -1 1 0 0 1 -1 0 1 0 1 -1 0 0 1 1 -1 -1 -1 -1 ```

## Algoritmos

`ranova` computes the regular p-value (in the `pValue` column of the `rmanova` table) using the F-statistic cumulative distribution function:

p-value = 1 – fcdf(F,v1,v2).

When the compound symmetry assumption is not satisfied, `ranova` uses a correction factor epsilon, ε, to compute the corrected p-values as follows:

p-value_corrected = 1 – fcdf(F,ε*v1,ε*v2).

The `mauchly` method tests for sphericity (hence, compound symmetry) and `epsilon` method returns the epsilon adjustment values.