# MIXED EFFECTS MODELS IN S AND S PLUS PDF

To Elisa and Laura To Mary Ellen, Barbara, and Michael PrefaceMixed-eﬀects models provide a ﬂexible and powerful to. Nonlinear Mixed-Effects Models. Front Matter. Pages PDF · Nonlinear Mixed-effects Models: Basic Concepts and Motivating Examples. Pages PDF | 5+ minutes read | On Sep 1, , V. J Carey and others published Mixed- Effects Models in S and S-Plus.

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Request PDF on ResearchGate | Mixed-Effect Models in S and S-plus | Linear Mixed-Effects * Theory and Computational Methods for LME Models * Structure of . The nlme library we developed for analyzing mixed-eﬀects models in implementations of the S language, including S-PLUS and R, provides the underlying. The software comes with a number of online manuals (in PDF format) . In the current version of S-plus linear and nonlinear mixed-effects models can be fitted .

However, various studies 13 — 15 have suggested that the variability of VF sensitivities is increased when sensitivity is lower in both normal and damaged eyes. A second assumption within the OLS framework is that the residuals the differences between the observed values and those predicted by the trend over time are uncorrelated.

In longitudinal data, it is common for temporal autocorrelation to be present even if no glaucomatous progression is observed.

The same effect can also occur when a linear fit is imposed on data that are actually changing in a nonlinear manner. As an example, the measure of interest in this case, the MD is assumed to change at a constant rate over time.

Therefore, given the logarithmic nature of the decibel dB scale used in perimetry, linear change in sensitivity over time implies that the proportion of remaining RGCs that die each year must remain constant. If we hypothesize an alternative formulation, where the actual number of remaining RGCs that die each year remains constant, resulting in a linear decline in structural measures such as retinal nerve fiber layer thickness, then based on the cross-sectional findings this would result in the loss of sensitivity in decibels appearing to accelerate exponentially.

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If a linear fit is placed over data that are accelerating downward exponentially, the residuals will tend to be positive in the center of the period and negative at either end of the sequence, causing potentially significant temporal autocorrelation. In the presence of heteroscedasticity or temporal autocorrelation, OLS estimators are no longer the best linear unbiased estimators.

Thus, statistics and confidence intervals from OLS may not be valid for drawing inference. Third, longitudinally collected data are often grouped by one or more grouping factors.

In the case of ophthalmic tests, data are grouped within an eye i. Observations taken within the same eye or from the same individual are likely to be more similar than observations taken from a different eye or person. This indicates the need for more sophisticated methods that take into account correlation among observations within the same group.

## Mixed-Effects Models Methods and Classes for S and S Plus

In this study, we use multilevel mixed-effects methods that account for group effects both within eyes and between fellow eyes of the same individual , as well as temporal autocorrelation of within-group errors, to generate more valid P values for assessing the significance of change in VF sequences from participants with glaucoma.

We then consider the use of a nonlinear model in which VF sensitivity declines exponentially with time based on the alternative formulation outlined above, and we determine whether this removes evidence of autocorrelation. We demonstrate and test these statistical methods with a relatively simple predictive model using only age as a predictor of the rate of glaucomatous field change.

However, the overall goal of this work is to aid in the development of better predictive models that will help clinicians manage patients with glaucoma. Methods Data Data from participants with suspected or early glaucoma or with high-risk ocular hypertension from the ongoing Portland Progression Project at Devers Eye Institute were used in this study. This study complies with the Health Insurance Portability and Accountability Act of and is in agreement with the provisions of the Declaration of Helsinki.

Consent was obtained from all participants after they were well informed about the risks and benefits of participation.

## Mixed-Effects Models in S and S-PLUS

Initially, participants were tested annually with various functional and structural tests. In , testing was switched to 6-month intervals for all participants.

Other exclusion criteria included any other disease or the use of any medications likely to affect the VF or having undergone intraocular surgery except for uncomplicated cataract surgery. This was accomplished by including earlier tests within a Guided Progression Analysis 30 sequence that included at least one test.

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Previously, we have shown that there is no difference in MD values generated by the Full Threshold and SITA Standard algorithms, 31 so there is no concern with using data from both. An optimal lens correction was placed before the tested eye, and an eye patch was used to occlude the fellow eye.

All subjects had previous experience with VF testing before entering the study, and most had undergone multiple previous tests. Statistical Analysis All statistical analyses were performed using statistical computing software R. These models are intended for grouped data in which the response variable is at least approximately continuous. All analyses included in the book were produced using version 3. Furthermore, the current version of the nlme library for R does not support the same range of graphics presentations as does the S-PLUS version.

Errata and updates of the material in the book will be made available on-line at the same sites. The book is divided into parts.

## Mixed Effects Models in S and S-Plus.pdf

Chapter 1 gives an overview of LME models, introducing some examples of grouped data and the type of analyses that applies to them. The theory and computational methods for LME models are the topics of Chapter 2. Chapter 3 describes the structure of grouped data and the many facilities available in the nlme library to display and summarize such data.

The model-building approach we propose is described and illustrated in detail in the context of LME models in Chapter 4. Extensions of the basic LME model to include variance functions and correlation structures for the within-group errors are considered in Chapter 5.

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Chapter 6 provides an overview of NLME models and some of the analysis tools available for them in nlme.Do there appear to be terms in the model that could be eliminated? If a term is inner to all Q grouping factors in a model. This change is incorporated in Figure 3. For some dogs the left side produces higher pixel densities while for other dogs the right side does.

All analyses included in the book were produced using version 3. These may be given as lme objects corresponding to each model. Because there are only four observations per subject. Note the change in the aspect ratio of the panels relative to the plots in parts c and d.

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