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Unbiased Recursive Partitioning: A Conditional Inference Framework
A unified framework for recursive partitioning is proposed which embeds tree-structured regression models into a well defined theory of conditional inference procedures and it is shown that the predicted accuracy of trees with early stopping is equivalent to the prediction accuracy of pruned trees with unbiased variable selection.
Regression Models for Count Data in R
The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in…
Bias in random forest variable importance measures: Illustrations, sources and a solution
An alternative implementation of random forests is proposed, that provides unbiased variable selection in the individual classification trees, that can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories.
Conditional variable importance for random forests
- C. Strobl, A. Boulesteix, T. Kneib, Thomas Augustin, A. Zeileis
- BiologyBMC Bioinformatics
- 11 July 2008
A new, conditional permutation scheme is developed for the computation of the variable importance measure that reflects the true impact of each predictor variable more reliably than the original marginal approach.
Beta Regression in R
The betareg package is described which provides the class of beta regressions in the R system for statistical computing and incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions.
Diagnostic Checking in Regression Relationships
A rich variety of diagnostic tests for these situations have been developed in the econometrics community, a collection of which has been implemented in the packages lmtest and strucchange covering the problems mentioned above.
kernlab - An S4 Package for Kernel Methods in R
The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, and a spectral clustering algorithm.
Implementing a Class of Permutation Tests: The coin Package
A computational framework is established in coin that likewise embeds the corresponding R functionality in a common S4 class structure with associated generic functions, inherit the flexibility of the underlying theory and conditional inference functions for important special cases can be set up easily.
Applied Econometrics with R
This is the first book on applied econometrics using the R system for statistical computing and graphics and provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research.
zoo: S3 Infrastructure for Regular and Irregular Time Series
A subclass "zooreg" embeds regular time series into the "zoo" framework and thus bridges the gap between regular and irregular time series classes in R.