David M Bates
Biographic Data
| ID | 115799 |
|---|---|
| NAME | David M Bates |
| GIVEN NAMES | David M |
| FAMILY NAME | Bates |
| SIGNATURE | BATES D M |
| AFFILIATIONS | University of Wisconsin–Madison |
| ORCID | 0000-0001-8316-9503 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 5 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1985 |
| LATEST PUBLICATION YEAR | 2017 |
| H-INDEX | 1 |
The cave of shadows: Addressing the human factor with generalized additive mixed models
Balancing Type I error and power in linear mixed models
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), pres…
Fitting Linear Mixed-Effects Models Using lme4
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 dev…
Mixed-effects modeling with crossed random effects for subjects and items
Mixed-Effects Models in Sand S-PLUS
This paperback edition is a reprint of the 2000 edition. This book provides an overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data. A unified model-building strategy for both linear and nonlinear models is presented and applied to the analysis of over 20 real datasets from a wide variety of areas, including pharmacoki…
Plant utilization: Patterns and prospects
Plant utilization: Patterns and prospects
Mixed-Effects Models in Sand S-PLUS
This paperback edition is a reprint of the 2000 edition. This book provides an overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data. A unified model-building strategy for both linear and nonlinear models is presented and applied to the analysis of over 20 real datasets from a wide variety of areas, including pharmacoki…
Mixed-effects modeling with crossed random effects for subjects and items
Fitting Linear Mixed-Effects Models Using lme4
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 dev…
The cave of shadows: Addressing the human factor with generalized additive mixed models
Balancing Type I error and power in linear mixed models
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), pres…
Computer Science (3 works) · Cognitive psychology (2 works) · Developmental psychology (2 works) · Geology (2 works) · Mathematics (2 works) · Psychology (2 works) · Psychometric Methodologies and Testing (2 works) · Statistical Methods and Bayesian Inference (2 works) · Advanced Statistical Methods and Models (1 works) · Advanced Statistical Modeling Techniques (1 works)