# Heteroscedastic Linear Mixed Models

    library(galamm)

At the moment, galamm supports group-wise heteroscedasticity in Gaussian response models. Referring to the model formulation outlined in the introductory vignette, the response model and nonlinear predictor can be easily combined in this particular case, to give

\[ y_{i} = \sum_{s=1}^{S} f_{s}\left(\mathbf{x}_{i}\right) + \sum_{l=2}^{L}\sum_{m=1}^{M_{l}} \eta_{m}^{(l)} \mathbf{z}^{(l)}_{im}{}^{'}\boldsymbol{\lambda}_{m}^{(l)} + \epsilon_{g(i)}, \]

where subscript \(i\) refers to the \(i\)th elementary unit of observation, i.e., the \(i\)th row in the dataframe. \(g(i)\) refers to the group to which the \(i\)th observation belongs, with each grouping having a separately estimated residual variance, \(\epsilon_{g} \sim N(0, \sigma_{g}^{2})\).

In the future, we plan to also support other types of residual terms, including autocorrelation and residuals that depend on continuous variables. Such features are currently supported by the R packages nlme (Pinheiro and Bates 2000), mgcv (Wood 2017), and gamlss (Rigby and Stasinopoulos 2005), however, of these only nlme provides computationally efficient estimation of mixed effects models with a large number of grouping levels, and only with strictly nested groups. If you are aware of other packages implementing such functionality, please let us know.

## Group-Wise Heteroscedasticity

The package includes a simulated dataset `hsced`, in which the residual variance varies between items.

    head(hsced)
    #>   id tp item         x          y
    #> 1  1  1    1 0.7448212  0.1608286
    #> 2  1  1    2 0.7109629  2.2947255
    #> 3  1  2    1 0.9507326 -0.4731834
    #> 4  1  2    2 0.4205776  1.1280379
    #> 5  1  3    1 0.1045820 -0.5129498
    #> 6  1  3    2 0.3872984  1.0515916

We specify the error structure using an additional formula object, `~ (1 | item)`, specifying that a different constraint term should be included per item.

    mod <- galamm(
      formula = y ~ x + (1 | id),
      dispformula = ~ (1 | item),
      data = hsced
    )

The output shows that for item 2, the residual variance is twice that of item 1.

    summary(mod)
    #> GALAMM fit by maximum marginal likelihood.
    #> Formula: y ~ x + (1 | id)
    #>    Data: hsced
    #>
    #>      AIC      BIC   logLik deviance df.resid
    #>   4126.3   4151.7  -2058.1   4116.3     1195
    #>
    #> Scaled residuals:
    #>     Min      1Q  Median      3Q     Max
    #> -5.6545 -0.7105  0.0286  0.6827  4.3261
    #>
    #> Random effects:
    #>  Groups   Name        Variance Std.Dev.
    #>  id       (Intercept) 0.9880   0.9940
    #>  Residual             0.9597   0.9796
    #> Number of obs: 1200, groups:  id, 200
    #>
    #> Variance function:
    #>     1     2
    #> 1.000 1.995
    #>
    #> Fixed effects:
    #>             Estimate Std. Error t value  Pr(>|t|)
    #> (Intercept)   0.1289     0.0992   1.299 1.938e-01
    #> x             0.7062     0.1213   5.822 5.819e-09

We can confirm that the lme function from the nlme package gives the same result. It reports the multiplies on the standard deviation scale, so since \(1.412369^2 = 1.995\), the results are identical.

    library(nlme)
    #>
    #> Attaching package: 'nlme'
    #> The following object is masked from 'package:lme4':
    #>
    #>     lmList
    mod_nlme <- lme(y ~ x,
      data = hsced, random = list(id = ~1),
      weights = varIdent(form = ~ 1 | item), method = "ML"
    )
    summary(mod_nlme)
    #> Linear mixed-effects model fit by maximum likelihood
    #>   Data: hsced
    #>       AIC      BIC   logLik
    #>   4126.28 4151.731 -2058.14
    #>
    #> Random effects:
    #>  Formula: ~1 | id
    #>         (Intercept)  Residual
    #> StdDev:   0.9940033 0.9796423
    #>
    #> Variance function:
    #>  Structure: Different standard deviations per stratum
    #>  Formula: ~1 | item
    #>  Parameter estimates:
    #>        1        2
    #> 1.000000 1.412369
    #> Fixed effects:  y ~ x
    #>                 Value  Std.Error  DF  t-value p-value
    #> (Intercept) 0.1288960 0.09927455 999 1.298379  0.1945
    #> x           0.7062301 0.12130578 999 5.821899  0.0000
    #>  Correlation:
    #>   (Intr)
    #> x -0.624
    #>
    #> Standardized Within-Group Residuals:
    #>         Min          Q1         Med          Q3         Max
    #> -4.00355402 -0.60661607  0.02357892  0.60903083  3.06299731
    #>
    #> Number of Observations: 1200
    #> Number of Groups: 200

The diagnostic plot also looks good.

    plot(mod, abline = c(0, 0))

Diagnostic plot for heteroscedastic model.

The quantile-quantile plot is also acceptable.

    qqmath(mod)

plot of chunk unnamed-chunk-6

We can compare the model to one with homoscedastic residuals.

    mod0 <- galamm(
      formula = y ~ x + (1 | id),
      data = hsced
    )

Reassuringly, the correct model is chosen in this simple simulated case.

    anova(mod, mod0)
    #> Data: hsced
    #> Models:
    #> mod0: y ~ x + (1 | id)
    #> mod: y ~ x + (1 | id)
    #>      npar    AIC    BIC  logLik deviance  Chisq Df Pr(>Chisq)
    #> mod0    4 4171.6 4191.9 -2081.8   4116.3
    #> mod     5 4126.3 4151.7 -2058.1   4116.3 47.281  1   6.15e-12 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# References

Pinheiro, Jose, and Douglas M Bates. 2000. Mixed-Effects Models in S and S-PLUS. Statistics and Computing. Springer.
Rigby, R. A., and D. M. Stasinopoulos. 2005. “Generalized Additive Models for Location, Scale and Shape.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 54 (3): 507–54. https://doi.org/10.1111/j.1467-9876.2005.00510.x.
Wood, Simon N. 2017. Generalized Additive Models: An Introduction with R. 2nd ed. Chapman and Hall/CRC.