Modeling Psychophysical Data in R by Kenneth Knoblauch
Many of the commonly used methods for modeling and fitting psychophysical data are special cases of statistical procedures of great power and generality, notably the Generalized Linear Model (GLM).-
A Primer of Ecology with R
-
Introductory Time Series with R
-
Bayesian Networks in R
-
ggplot2
-
A Users Guide to Network Analysis in R
-
Bayesian Computation with R
-
Biostatistics with R
-
R For Marketing Research and Analytics
-
Data Manipulation with R
-
Applied Spatial Data Analysis with R
-
R Coding for Ecology
-
Bayesian Cost-Effectiveness Analysis with the R package BCEA
-
Geostatistics for Compositional Data with R
-
Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R
-
Behavioral Research Data Analysis with R
-
Discrete Choice Analysis with R
-
R by Example
-
Semiparametric Regression with R
-
Heart Rate Variability Analysis with the R package RHRV
-
Elements of Copula Modeling with R
-
Modern Optimization with R
-
Cultural Analytics in R: A Tidy Approach
-
An Introduction to Web Mining
-
Numerical Ecology with R
-
Morphometrics with R
-
Analysis of Integrated and Cointegrated Time Series with R
-
Multistate Analysis of Life Histories with R
-
Computerized Adaptive and Multistage Testing with R
-
Analyzing Compositional Data with R
-
Solving Differential Equations in R
-
Functional and Phylogenetic Ecology in R
-
Functional Data Analysis with R and MATLAB
-
Mixture and Hidden Markov Models with R
-
Singular Spectrum Analysis with R
-
Retirement Income Recipes in R
-
Quality Control with R
-
Analysis of Phylogenetics and Evolution with R
-
Dynamic Linear Models with R
-
Applied Survival Analysis Using R
-
Magnetic Resonance Brain Imaging
Although the applications of R presented in this text are focused on the analysis of psychophysical data, the methodology is generalizable to many other areas in statisticsFor example, coverage of solving equations by maximum likelihood and the various general linear model functions are applicable in many situations which occur in statistical data analysis. I found the discussion of ROC analysis to be very useful in many other areas of statistics also. Therefore, I would recommend this text to anyone who works with, or is interested in, psychophysical data or even to anyone who wants to increase their knowledge of R.
Technometrics, 56:1 2014
Laurence T. Maloney is Professor of Psychology and Neural Science at New York University. His research focusses on applications of mathematical models to perception, motor control and decision making.
| SKU | Unavailable |
| ISBN 13 | 9781461444749 |
| ISBN 10 | 1461444748 |
| Title | Modeling Psychophysical Data in R |
| Author | Kenneth Knoblauch |
| Series | Use R! |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 2012-09-01 |
| Number of pages | 365 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |