# ranova

**Class: **RepeatedMeasuresModel

Repeated measures analysis of variance

## Description

## Input Arguments

`rm`

— Repeated measures model

`RepeatedMeasuresModel`

object

Repeated measures model, returned as a `RepeatedMeasuresModel`

object.

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

.

`WM`

— Model specifying responses

`'separatemeans'`

(default) | *r*-by-*nc* contrast matrix | character vector or string scalar that defines a model
specification

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.

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

**Data Types: **`single`

| `double`

| `char`

| `string`

## Output Arguments

`ranovatbl`

— Results of repeated measures anova

table

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 Name | Definition |
---|---|

`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.

`A`

— Specification based on between-subjects model

matrix | cell array

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.

**Data Types: **`single`

| `double`

| `cell`

`C`

— Specification based on within-subjects model

matrix | cell array

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.

**Data Types: **`single`

| `double`

| `cell`

`D`

— Hypothesis value

0

Hypothesis value, returned as 0.

## Examples

### Repeated Measures Analysis of Variance

Load the sample data.

`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 (150–3)*(4–1) = 441. `ranova`

computes the last three $$p$$-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.

### Longitudinal Data

Load the sample data.

`load('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 $$p$$-value of 2.6198e–05 indicates that there is a significant effect of time on blood pressure. The $$p$$ -value of 0.40063 indicates that there is no significant gender-time interaction.

### Specify the Within-Subjects Model

Load the sample data.

`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. Hypothetically, the response can be results of a memory test. The within-subject variable `w1`

can be the type of exercise the subject does before the test and `w2`

can be the different points in the day the subject takes the memory test. So, one subject does two different type of exercises A and B before taking the test and takes the test at four different times on different days. For each subject, the measurements are taken under these conditions:

Exercise to perform before the test: A B A B A B A B

Test time: 1 1 2 2 3 3 4 4

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=`*6×1 cell array*
{[1 0 0 0 0 0 0 0]}
{[0 1 0 0 0 0 0 0]}
{[0 0 1 0 0 0 0 0]}
{2x8 double }
{[0 0 0 0 0 1 0 0]}
{2x8 double }

`C=`*1×3 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

## Algorithms

`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*,*v*_{1},*v*_{2}).

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***v*_{1},*ε***v*_{2}).

The `mauchly`

method tests for sphericity (hence,
compound symmetry) and `epsilon`

method returns the
epsilon adjustment values.

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