# randomEffects

Estimates of random effects and related statistics

## Syntax

``B = randomEffects(lme)``
``````[B,Bnames] = randomEffects(lme)``````
``````[B,Bnames,stats] = randomEffects(lme)``````
``````[B,Bnames,stats] = randomEffects(lme,Name,Value)``````

## Description

example

````B = randomEffects(lme)` returns the estimates of the best linear unbiased predictors (BLUPs) of random effects in the linear mixed-effects model `lme`.```

example

``````[B,Bnames] = randomEffects(lme)``` also returns the names of the coefficients in `Bnames`. Each name corresponds to a coefficient in `B`.```

example

``````[B,Bnames,stats] = randomEffects(lme)``` also returns the estimated BLUPs of random effects in the linear mixed-effects model `lme` and related statistics.```

example

``````[B,Bnames,stats] = randomEffects(lme,Name,Value)``` also returns the BLUPs of random effects in the linear mixed-effects model `lme` and related statistics with additional options specified by one or more `Name,Value` pair arguments.```

## Examples

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`load carbig`

Fit a linear mixed-effects model for miles per gallon (MPG), with fixed effects for acceleration and horsepower, and potentially correlated random effects for intercept and acceleration, grouped by the model year. First, store the data in a table.

`tbl = table(Acceleration,Horsepower,Model_Year,MPG);`

Fit the model.

`lme = fitlme(tbl, 'MPG ~ Acceleration + Horsepower + (Acceleration|Model_Year)');`

Compute the BLUPs of the random-effects coefficients and display the names of the corresponding random effects.

`[B,Bnames] = randomEffects(lme)`
```B = 26×1 3.1270 -0.2426 -1.6532 -0.0086 1.2075 -0.2179 4.4107 -0.4887 -1.3103 -0.0208 ⋮ ```
```Bnames=26×3 table Group Level Name ______________ ______ ________________ {'Model_Year'} {'70'} {'(Intercept)' } {'Model_Year'} {'70'} {'Acceleration'} {'Model_Year'} {'71'} {'(Intercept)' } {'Model_Year'} {'71'} {'Acceleration'} {'Model_Year'} {'72'} {'(Intercept)' } {'Model_Year'} {'72'} {'Acceleration'} {'Model_Year'} {'73'} {'(Intercept)' } {'Model_Year'} {'73'} {'Acceleration'} {'Model_Year'} {'74'} {'(Intercept)' } {'Model_Year'} {'74'} {'Acceleration'} {'Model_Year'} {'75'} {'(Intercept)' } {'Model_Year'} {'75'} {'Acceleration'} {'Model_Year'} {'76'} {'(Intercept)' } {'Model_Year'} {'76'} {'Acceleration'} {'Model_Year'} {'77'} {'(Intercept)' } {'Model_Year'} {'77'} {'Acceleration'} ⋮ ```

Since intercept and acceleration have potentially correlated random effects, grouped by model year of the cars, `randomEffects` creates a separate row for intercept and acceleration at each level of the grouping variable.

Compute the covariance parameters of the random effects.

`[~,~,stats] = covarianceParameters(lme)`
```stats=2×1 cell array {3x7 classreg.regr.lmeutils.titleddataset} {1x5 classreg.regr.lmeutils.titleddataset} ```
`stats{1}`
```ans = COVARIANCE TYPE: FULLCHOLESKY Group Name1 Name2 Type Estimate Lower Upper Model_Year {'(Intercept)' } {'(Intercept)' } {'std' } 3.3475 1.2862 8.7119 Model_Year {'Acceleration'} {'(Intercept)' } {'corr'} -0.87971 -0.98501 -0.29675 Model_Year {'Acceleration'} {'Acceleration'} {'std' } 0.33789 0.1825 0.62558 ```

The correlation value suggests that random effects seem negatively correlated. Plot the random effects for intercept versus acceleration to confirm this.

`plot(B(1:2:end),B(2:2:end),'r*')` `load('fertilizer.mat');`

The dataset array includes data from a split-plot experiment, where soil is divided into three blocks based on the soil type: sandy, silty, and loamy. Each block is divided into five plots, where five different types of tomato plants (cherry, heirloom, grape, vine, and plum) are randomly assigned to these plots. The tomato plants in the plots are then divided into subplots, where each subplot is treated by one of four fertilizers. This is simulated data.

Store the data in a dataset array called `ds`, for practical purposes, and define `Tomato`, `Soil`, and `Fertilizer` as categorical variables.

```ds = fertilizer; ds.Tomato = nominal(ds.Tomato); ds.Soil = nominal(ds.Soil); ds.Fertilizer = nominal(ds.Fertilizer);```

Fit a linear mixed-effects model, where `Fertilizer` and `Tomato` are the fixed-effects variables, and the mean yield varies by the block (soil type), and the plots within blocks (tomato types within soil types) independently.

`lme = fitlme(ds,'Yield ~ Fertilizer * Tomato + (1|Soil) + (1|Soil:Tomato)');`

Compute the BLUPs and related statistics for random effects.

`[~,~,stats] = randomEffects(lme)`
```stats = RANDOM EFFECT COEFFICIENTS: DFMETHOD = 'RESIDUAL', ALPHA = 0.05 Group Level Name Estimate SEPred tStat DF pValue Lower Upper {'Soil' } {'Loamy' } {'(Intercept)'} 1.0061 2.3374 0.43044 40 0.66918 -3.718 5.7303 {'Soil' } {'Sandy' } {'(Intercept)'} -1.5236 2.3374 -0.65181 40 0.51825 -6.2477 3.2006 {'Soil' } {'Silty' } {'(Intercept)'} 0.51744 2.3374 0.22137 40 0.82593 -4.2067 5.2416 {'Soil:Tomato'} {'Loamy Cherry' } {'(Intercept)'} 12.46 7.1765 1.7362 40 0.090224 -2.0443 26.964 {'Soil:Tomato'} {'Loamy Grape' } {'(Intercept)'} -2.6429 7.1765 -0.36827 40 0.71461 -17.147 11.861 {'Soil:Tomato'} {'Loamy Heirloom'} {'(Intercept)'} 16.681 7.1765 2.3244 40 0.025269 2.1766 31.185 {'Soil:Tomato'} {'Loamy Plum' } {'(Intercept)'} -5.0172 7.1765 -0.69911 40 0.48853 -19.522 9.4872 {'Soil:Tomato'} {'Loamy Vine' } {'(Intercept)'} -4.6874 7.1765 -0.65316 40 0.51739 -19.192 9.8169 {'Soil:Tomato'} {'Sandy Cherry' } {'(Intercept)'} -17.393 7.1765 -2.4235 40 0.019987 -31.897 -2.8882 {'Soil:Tomato'} {'Sandy Grape' } {'(Intercept)'} -7.3679 7.1765 -1.0267 40 0.31075 -21.872 7.1364 {'Soil:Tomato'} {'Sandy Heirloom'} {'(Intercept)'} -8.621 7.1765 -1.2013 40 0.23671 -23.125 5.8833 {'Soil:Tomato'} {'Sandy Plum' } {'(Intercept)'} 7.669 7.1765 1.0686 40 0.29165 -6.8353 22.173 {'Soil:Tomato'} {'Sandy Vine' } {'(Intercept)'} 0.28246 7.1765 0.039359 40 0.9688 -14.222 14.787 {'Soil:Tomato'} {'Silty Cherry' } {'(Intercept)'} 4.9326 7.1765 0.68732 40 0.49585 -9.5718 19.437 {'Soil:Tomato'} {'Silty Grape' } {'(Intercept)'} 10.011 7.1765 1.3949 40 0.17073 -4.4935 24.515 {'Soil:Tomato'} {'Silty Heirloom'} {'(Intercept)'} -8.0599 7.1765 -1.1231 40 0.2681 -22.564 6.4444 {'Soil:Tomato'} {'Silty Plum' } {'(Intercept)'} -2.6519 7.1765 -0.36952 40 0.71369 -17.156 11.852 {'Soil:Tomato'} {'Silty Vine' } {'(Intercept)'} 4.405 7.1765 0.6138 40 0.54282 -10.099 18.909 ```

The first three rows contain the random-effects estimates and the statistics for the three levels, `Loamy`, `Sandy`, and `Silty` of the grouping variable `Soil`. The corresponding $p$-values 0.66918, 0.51825, and 0.82593 indicate that these random-effects are not significantly different from 0. The following 15 rows include the BLUPS of random-effects estimates for the intercept, grouped by the variable Tomato nested in Soil, i.e. interaction of `Tomato` and `Soil`.

`load shift`

Fit a linear mixed-effects model with a random intercept grouped by operator, to assess if there is a significant difference in the performance according to the time of the shift. Use the restricted maximum likelihood method.

`lme = fitlme(shift,'QCDev ~ Shift + (1|Operator)');`

Compute the 99% confidence intervals for random effects using the residuals option to compute the degrees of freedom. This is the default method.

`[~,~,stats] = randomEffects(lme,'Alpha',0.01)`
```stats = RANDOM EFFECT COEFFICIENTS: DFMETHOD = 'RESIDUAL', ALPHA = 0.01 Group Level Name Estimate SEPred tStat DF pValue Lower Upper {'Operator'} {'1'} {'(Intercept)'} 0.57753 0.90378 0.63902 12 0.53482 -2.1831 3.3382 {'Operator'} {'2'} {'(Intercept)'} 1.1757 0.90378 1.3009 12 0.21772 -1.5849 3.9364 {'Operator'} {'3'} {'(Intercept)'} -2.1715 0.90378 -2.4027 12 0.033352 -4.9322 0.58909 {'Operator'} {'4'} {'(Intercept)'} 2.3655 0.90378 2.6174 12 0.022494 -0.39511 5.1261 {'Operator'} {'5'} {'(Intercept)'} -1.9472 0.90378 -2.1546 12 0.052216 -4.7079 0.81337 ```

Compute the 99% confidence intervals for random effects using the Satterthwaite approximation to compute the degrees of freedom.

`[~,~,stats] = randomEffects(lme,'DFMethod','satterthwaite','Alpha',0.01)`
```stats = RANDOM EFFECT COEFFICIENTS: DFMETHOD = 'SATTERTHWAITE', ALPHA = 0.01 Group Level Name Estimate SEPred tStat DF pValue Lower Upper {'Operator'} {'1'} {'(Intercept)'} 0.57753 0.90378 0.63902 6.4253 0.5449 -2.684 3.839 {'Operator'} {'2'} {'(Intercept)'} 1.1757 0.90378 1.3009 6.4253 0.23799 -2.0858 4.4372 {'Operator'} {'3'} {'(Intercept)'} -2.1715 0.90378 -2.4027 6.4253 0.050386 -5.433 1.09 {'Operator'} {'4'} {'(Intercept)'} 2.3655 0.90378 2.6174 6.4253 0.037302 -0.89598 5.627 {'Operator'} {'5'} {'(Intercept)'} -1.9472 0.90378 -2.1546 6.4253 0.071626 -5.2087 1.3142 ```

The Satterthwaite method usually produces smaller values for the degrees of freedom (`DF`), which results in larger p-values (`pValue`) and larger confidence intervals (`Lower` and `Upper`) for the random-effects estimates.

## Input Arguments

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Linear mixed-effects model, specified as a `LinearMixedModel` object constructed using `fitlme` or `fitlmematrix`.

### Name-Value Arguments

Specify optional pairs of arguments as `Name1=Value1,...,NameN=ValueN`, where `Name` is the argument name and `Value` is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose `Name` in quotes.

Example: ```[B,Bnames,stats] = randomEffects(lme,'Alpha',0.01)```

Significance level, specified as the comma-separated pair consisting of `'Alpha'` and a scalar value in the range 0 to 1. For a value α, the confidence level is 100*(1–α)%.

For example, for 99% confidence intervals, you can specify the confidence level as follows.

Example: `'Alpha',0.01`

Data Types: `single` | `double`

Method for computing approximate degrees of freedom for the t-statistics that test the random-effects coefficients against 0, specified as the comma-separated pair consisting of `'DFMethod'` and one of the following.

 `'residual'` Default. The degrees of freedom are assumed to be constant and equal to n – p, where n is the number of observations and p is the number of fixed effects. `'satterthwaite'` Satterthwaite approximation. `'none'` All degrees of freedom are set to infinity.

For example, you can specify the Satterthwaite approximation as follows.

Example: `'DFMethod','satterthwaite'`

## Output Arguments

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Estimated best linear unbiased predictors of random effects of linear mixed-effects model `lme`, returned as a column vector.

Suppose `lme` has R grouping variables g1, g2, ..., gR, with levels m1, m2, ..., mR, respectively. Also suppose q1, q2, ..., qR are the lengths of the random-effects vectors that are associated with g1, g2, ..., gR, respectively. Then, `B` is a column vector of length q1*m1 + q2*m2 + ... + qR*mR.

`randomEffects` creates `B` by concatenating the best linear unbiased predictors of random-effects vectors corresponding to each level of each grouping variable as ```[g1level1; g1level2; ...; g1levelm1; g2level1; g2level2; ...; g2levelm2; ...; gRlevel1; gRlevel2; ...; gRlevelmR]'```.

Names of random-effects coefficients in `B`, returned as a table.

Estimates of random effects BLUPs and related statistics, returned as a dataset array that has one row for each of the fixed effects and one column for each of the following statistics.

 `Group` Grouping variable associated with the random effect `Level` Level within the grouping variable corresponding to the random effect `Name` Name of the random-effect coefficient `Estimate` Best linear unbiased predictor (BLUP) of random effect `SEPred` Standard error of the estimate (BLUP minus random effect) `tStat` t-statistic for a test that the random effect is zero `DF` Estimated degrees of freedom for the t-statistic `pValue` p-value for the t-statistic `Lower` Lower limit of a 95% confidence interval for the random effect `Upper` Upper limit of a 95% confidence interval for the random effect

## Version History

Introduced in R2013b