Douglas M. Bates

University of Wisconsin-Madison

89Publications

32H-index

49.7kCitations

Publications 89

Newest

#1Hua Zhou (UCLA: University of California, Los Angeles)H-Index: 19

#2Janet S. Sinsheimer (UCLA: University of California, Los Angeles)H-Index: 42

Last.Kenneth Lange (UCLA: University of California, Los Angeles)H-Index: 56

view all 15 authors...

Statistical methods for genome-wide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OpenMendel project (https://openmendel.github.io). It aims to (1) enable in...

#1Harald Baayen (University of Tübingen)H-Index: 7

#2Shravan Vasishth (University of Potsdam)H-Index: 23

Last.Douglas M. Bates (UW: University of Wisconsin-Madison)H-Index: 32

view all 4 authors...

Abstract Generalized additive mixed models are introduced as an extension of the generalized linear mixed model which makes it possible to deal with temporal autocorrelational structure in experimental data. This autocorrelational structure is likely to be a consequence of learning, fatigue, or the ebb and flow of attention within an experiment (the ‘human factor’). Unlike molecules or plots of barley, subjects in psycholinguistic experiments are intelligent beings that depend for their survival...

#1Hannes Matuschek (University of Potsdam)H-Index: 5

#2Reinhold Kliegl (University of Potsdam)H-Index: 59

Last.Douglas M. Bates (UW: University of Wisconsin-Madison)H-Index: 32

view all 5 authors...

Abstract Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments. Although LMMs have many advantages over ANOVA, like ANOVAs, setting them up for data analysis also requires some care. One simple option, when numerically possible, is to fit the full variance-covariance structure of random effects (the maximal model; Barr, Levy, Scheepers & Tily, 2013), presumably to keep Type I error down to the n...

#1Martin MächlerH-Index: 16

#2Douglas M. BatesH-Index: 32

Linear algebra is at the core of many areas of statistical computing and from its inception the S language has supported numerical linear algebra via a matrix data type and several functions and operators, such as %*%, qr, chol, and solve. However, these data types and functions do not provide direct access to all of the facilities for efficient manipulation of dense matrices, as provided by the Lapack subroutines, and they do not provide for manipulation of sparse matrices. The Matrix package p...

#1Harald BaayenH-Index: 7

#2Shravan VasishthH-Index: 23

Last.Reinhold KlieglH-Index: 59

view all 4 authors...

Generalized additive mixed models are introduced as an extension of the generalized linear mixed model which makes it possible to deal with temporal autocorrelational structure in experimental data. This autocorrelational structure is likely to be a consequence of learning, fatigue, or the ebb and flow of attention within an experiment (the `human factor'). Unlike molecules or plots of barley, subjects in psycholinguistic experiments are intelligent beings that depend for their survival on const...

#1Harald BaayenH-Index: 7

#2Shravan VasishthH-Index: 23

Last.Reinhold KlieglH-Index: 59

view all 4 authors...

Unlike molecules or plots of barley, subjects in psycholinguistic experiments are intelligent beings that depend for their survival on constant adaptation to their environment. This study presents three data sets documenting the presence of adaptive processes in psychological experiments. These adaptive processes leave a statistical footprint in the form of autocorrelations in the residual error associated with by-subject time series of trial-to-trial responses. Generalized additive mixed models...

#1Douglas M. BatesH-Index: 32

#2Martin MächlerH-Index: 16

Last.Steve WalkerH-Index: 2

view all 4 authors...

Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evalua...

#1Douglas M. BatesH-Index: 32

#2Martin MächlerH-Index: 16

Last.Steven WalkerH-Index: 1

view all 4 authors...

#1Douglas M. BatesH-Index: 32

#2Reinhold KlieglH-Index: 59

Last.Harald Baayen (University of Tübingen)H-Index: 7

view all 4 authors...

The analysis of experimental data with mixed-effects models requires decisions about the specification of the appropriate random-effects structure. Recently, Barr, Levy, Scheepers, and Tily, 2013 recommended fitting `maximal' models with all possible random effect components included. Estimation of maximal models, however, may not converge. We show that failure to converge typically is not due to a suboptimal estimation algorithm, but is a consequence of attempting to fit a model that is too com...

#1Douglas M. Bates (UW: University of Wisconsin-Madison)H-Index: 32

The analysis of longitudinal data may require a mixed-effects model, incorporating parameters for fixed effects associated with the whole population and also parameters describing distributions of random effects associated with individual subjects. These may enter the model nonlinearly, as in compartment models used in pharmacokinetics. Maximum likelihood estimation is carried out by numerical optimization. Keywords: repeated measures; growth curves; pharmacokinetics; compartment models; random ...

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