Residual plots for repeated measures

Weiss, Robert E.; Lázaro, Carlos Gallego

doi:10.1002/sim.4780110110

Cited by 31 publications

(18 citation statements)

References 18 publications

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“…As a result, the proposed plot can compare effectively the fits of several models. On the other hand, the parallel plots of residuals proposed by Weiss and Lazaro (1992) focus on the individual observations from each subject. The parallel plots need to plot all residuals simultaneously.…”

Section: Discussionmentioning

confidence: 99%

“…As expected, Model 3 shows systematic systematic departures from the Y ¼ X line. Figure 6 also shows two distinct observations that seem to be outliers and were also identified by Weiss and Lazaro (1992) using the parallel residual plots. Figure 7 is the normalized residual plot where the Y axis is the normalized residual and the X axis is the subject ID.…”

Section: Growth Datamentioning

confidence: 95%

“…The idea of using residuals in repeated measures data has been introduced by Weiss and Lazaro (1992). They proposed parallel plots of residuals to check model fits and to identify outlying observations.…”

Section: Introductionmentioning

confidence: 99%

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Model Diagnostic Plots for Repeated Measures Data

Park¹,

Lee

2004

Biometrical J

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SummaryIn the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. Though these models have been widely used, not many studies have been performed in model diagnostic areas. In this paper, we propose simple residual plots to investigate the goodness of model fit for repeated measures data. Here, we mainly focus on the mean model diagnostics. The proposed residual plots are based on the quantile-quantile(Q-Q) plots of a c 2 distribution and a normal distribution. In particular, the proposed model is useful in comparing several models simultaneously. The proposed method is illustrated using two examples.

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Section: Discussionmentioning

confidence: 99%

Section: Growth Datamentioning

confidence: 95%

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Model Diagnostic Plots for Repeated Measures Data

Park¹,

Lee

2004

Biometrical J

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“…A similar approach was applied earlier by Zellner and Moulton (1985) in model selection. Weiss and Lazaro (1992) and Weiss, Cho, and Yanuzzi (1999) detected outliers in random-effects models for repeated measures data, using a related approach based on a comparison of prior and posterior distributions of parameters at various levels of the model. For a general approach to sensitivity analysis, including outlier detection, see the article by Weiss (1996).…”

Section: Review Of Bayesian Outlier Diagnosticsmentioning

confidence: 99%

Posterior Predictive Outlier Detection Using Sample Reweighting

Zaslavsky

Bradlow

2010

Journal of Computational and Graphical Statistics

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To facilitate checking and improvement of a Bayesian model, we define an outlier as an observation or group of observations that is "surprising" relative to its predictive distribution, under the model, given the remainder of the data. Hence outlyingness can be measured by the posterior predictive case-deleted p-value of any interesting scalar summary of the (possibly multivariate) observation. It is also sometimes useful to condition on a part of the data for the potentially outlying case, such as the pattern of missing data, thus defining a conditional outlier.When parameters of interest have been sampled from their posterior distribution, the case-deleted p-value can be calculated by reweighting the sample to reflect deletion of the target observation and then drawing from the predictive distribution of the observation, facilitating huge computational savings. One efficient extension to the basic reweighting approach exploits the conditional independence structure of most hierarchical models. Another extension uses weighted systematic sampling to accommodate the dependence structure of the MCMC sample.Outlier checks are illustrated in hierarchical models for two datasets, a standard linear hierarchical model for rat growth and a complex ordinal model for survey data with nonignorably missing responses.Sample code and data for the rat growth example are available as an on-line supplement.

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“…Some of the most commonly used graphs for identifying the mean and mixed-effects models for longitudinal data are the profile plots, variograms, and scatterplot matrices (Weiss & Lazaro, 1992;Diggle et al, 1994;Dawson et al, 1997). The partial scatterplot matrices have been used lately in identifying AR and Markov-type structures for covariance of longitudinal data (Zimmerman, 2000;Davison & Sardy, 2000;Zimmerman & Núñez-Antón, 2001).…”

Section: Introductionmentioning

confidence: 99%

Graphical Diagnostics for Modeling Unstructured Covariance Matrices

Pourahmadi¹

2002

Int Statistical Rev

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Prudent statistical analysis of correlated data requires accounting for the correlation among the measurements. Specifying a form for the covariance matrix of the data could reduce the high number of parameters of the covariance and increase efficiency of the inferences about the regression parameters. Motivated by the success of ordinary, partial and inverse correlograms in identifying parsimonious models for stationary time series, we introduce generalizations of these plots for nonstationary data. Their roles in detecting heterogeneity and correlation of the data and identifying parsimonious models for the covariance matrix are illuminated using a longitudinal dataset. Decomposition of a covariance matrix into "variance" and "dependence" components provides the necessary ingredients for the proposed graphs. This amounts to replacing a 3-D correlation plot by a pair of 2-D plots, providing complementary information about dependence and heterogeneity. Models identified and fitted using the variance-correlation decomposition of a covariance matrix are not guaranteed to be positive definite, but those using the modified Cholesky decomposition are. Limitations of our graphical diagnostics for general multivariate data where the measurements are not (time-) ordered are discussed.

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Residual plots for repeated measures

Cited by 31 publications

References 18 publications

Model Diagnostic Plots for Repeated Measures Data

Model Diagnostic Plots for Repeated Measures Data

Posterior Predictive Outlier Detection Using Sample Reweighting

Graphical Diagnostics for Modeling Unstructured Covariance Matrices

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