3,811 research outputs found
lmerTest Package: Tests in Linear Mixed Effects Models
One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects. We have implemented the Satterthwaite's method for approximating degrees of freedom for the t and F tests. We have also implemented the construction of Type I - III ANOVA tables. Furthermore, one may also obtain the summary as well as the anova table using the Kenward-Roger approximation for denominator degrees of freedom (based on the KRmodcomp function from the pbkrtest package). Some other convenient mixed model analysis tools such as a step method, that performs backward elimination of nonsignificant effects - both random and fixed, calculation of population means and multiple comparison tests together with plot facilities are provided by the package as well
An improved algorithm for stoichiometric network analysis: theory and applications
Motivation: Genome scale analysis of the metabolic network of a microorganism is a major challenge in bioinformatics. The combinatorial explosion, which occurs during the construction of elementary fluxes (non-redundant pathways) requires sophisticated and efficient algorithms to tackle the problem. Results: Mathematically, the calculation of elementary fluxes amounts to characterizing the space of solutions to a mixed system of linear equalities, given by the stoichiometry matrix, and linear inequalities, arising from the irreversibility of some or all of the reactions in the network. Previous approaches to this problem have iteratively solved for the equalities while satisfying the inequalities throughout the process. In an extension of previous work, here we consider the complementary approach and derive an algorithm which satisfies the inequalities one by one while staying in the space of solution of the equality constraints. Benchmarks on different subnetworks of the central carbon metabolism of Escherichia coli show that this new approach yields a significant reduction in the execution time of the calculation. This reduction arises since the odds that an intermediate elementary flux already fulfills an additional inequality are larger than when having to satisfy an additional equality constraint. Availability: The code is available upon request. Supplementary information: Pseudo code and a Mathematica implementation of the algorithm is on the OUP server. Contact: [email protected]; [email protected]
PyDEC: Software and Algorithms for Discretization of Exterior Calculus
This paper describes the algorithms, features and implementation of PyDEC, a
Python library for computations related to the discretization of exterior
calculus. PyDEC facilitates inquiry into both physical problems on manifolds as
well as purely topological problems on abstract complexes. We describe
efficient algorithms for constructing the operators and objects that arise in
discrete exterior calculus, lowest order finite element exterior calculus and
in related topological problems. Our algorithms are formulated in terms of
high-level matrix operations which extend to arbitrary dimension. As a result,
our implementations map well to the facilities of numerical libraries such as
NumPy and SciPy. The availability of such libraries makes Python suitable for
prototyping numerical methods. We demonstrate how PyDEC is used to solve
physical and topological problems through several concise examples.Comment: Revised as per referee reports. Added information on scalability,
removed redundant text, emphasized the role of matrix based algorithms,
shortened length of pape
Mixtures of g-priors in Generalized Linear Models
Mixtures of Zellner's g-priors have been studied extensively in linear models
and have been shown to have numerous desirable properties for Bayesian variable
selection and model averaging. Several extensions of g-priors to Generalized
Linear Models (GLMs) have been proposed in the literature; however, the choice
of prior distribution of g and resulting properties for inference have received
considerably less attention. In this paper, we unify mixtures of g-priors in
GLMs by assigning the truncated Compound Confluent Hypergeometric (tCCH)
distribution to 1/(1 + g), which encompasses as special cases several mixtures
of g-priors in the literature, such as the hyper-g, Beta-prime, truncated
Gamma, incomplete inverse-Gamma, benchmark, robust, hyper-g/n, and intrinsic
priors. Through an integrated Laplace approximation, the posterior distribution
of 1/(1 + g) is in turn a tCCH distribution, and approximate marginal
likelihoods are thus available analytically, leading to "Compound
Hypergeometric Information Criteria" for model selection. We discuss the local
geometric properties of the g-prior in GLMs and show how the desiderata for
model selection proposed by Bayarri et al, such as asymptotic model selection
consistency, intrinsic consistency, and measurement invariance may be used to
justify the prior and specific choices of the hyper parameters. We illustrate
inference using these priors and contrast them to other approaches via
simulation and real data examples. The methodology is implemented in the R
package BAS and freely available on CRAN
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