Journal of Statistical Software http://www.jstatsoft.org/rss Wed, 02 Sep 2015 10:31:13 GMT Wed, 02 Sep 2015 10:31:13 GMT Most recent publications from the Journal of Statistical Software dawai: An R Package for Discriminant Analysis with Additional Information http://www.jstatsoft.org/v66/i10/paper Vol. 66, Issue 10, Aug 2015

Abstract:

The incorporation of additional information into discriminant rules is receiving in- creasing attention as the rules including this information perform better than the usual rules. In this paper we introduce an R package called dawai, which provides the functions that allow to define the rules that take into account this additional information expressed in terms of restrictions on the means, to classify the samples and to evaluate the accuracy of the results. Moreover, in this paper we extend the results and definitions given in previous papers (Fernández, Rueda, and Salvador 2006, Conde, Fernández, Rueda, and Salvador 2012, Conde, Salvador, Rueda, and Fernández 2013) to the case of unequal co-variances among the populations, and consequently define the corresponding restricted quadratic discriminant rules. We also define estimators of the accuracy of the rules for the general more than two populations case. The wide range of applications of these procedures is illustrated with two data sets from two different fields, i.e., biology and pattern recognition.

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Thu, 27 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/i10
SSMMATLAB: A Set of MATLAB Programs for the Statistical Analysis of State Space Models http://www.jstatsoft.org/v66/i09/paper Vol. 66, Issue 9, Aug 2015

Abstract:

This article discusses and describes SSMMATLAB, a set of programs written by the author in MATLAB for the statistical analysis of state space models. The state space model considered is very general. It may have univariate or multivariate observations, time-varying system matrices, exogenous inputs, regression effects, incompletely specified initial conditions, such as those that arise with cointegrated VARMA models, and missing values. There are functions to put frequently used models, such as multiplicative VARMA models, VARMAX models in echelon form, cointegrated VARMA models, and univariate structural or ARIMA model-based unobserved components models, into state space form. There are also functions to implement the Hillmer-Tiao canonical decomposition and the smooth trend and cycle estimation proposed by Gómez (2001). Once the model is in state space form, other functions can be used for likelihood evaluation, model estimation, forecasting and smoothing. A set of examples is presented in the SSMMATLAB manual to illustrate the use of these functions.

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Thu, 27 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/i09
The R Package threg to Implement Threshold Regression Models http://www.jstatsoft.org/v66/i08/paper Vol. 66, Issue 8, Aug 2015

Abstract:

This paper introduces the R package threg, which implements the estimation procedure of a threshold regression model, which is based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data, and serves as an important alternative to the Cox proportional hazards model.
This new package includes four functions: threg, and the methods hr, predict and plot for ‘threg’ objects returned by threg. The threg function is the model-fitting function which is used to calculate regression coefficient estimates, asymptotic standard errors and p values. The hr method for ‘threg’ objects is the hazard-ratio calculation function which provides the estimates of hazard ratios at selected time points for specified scenarios (based on given categories or value settings of covariates). The predict method for ‘threg objects is used for prediction. And the plot method for ‘threg’ objects provides plots for curves of estimated hazard functions, survival functions and probability density functions of the first-hitting-time; function curves corresponding to different scenarios can be overlaid in the same plot for comparison to give additional research insights.

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Thu, 27 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/i08
Consistent and Clear Reporting of Results from Diverse Modeling Techniques: The A3 Method http://www.jstatsoft.org/v66/i07/paper Vol. 66, Issue 7, Aug 2015

Abstract:

The measurement and reporting of model error is of basic importance when constructing models. Here, a general method and an R package, A3, are presented to support the assessment and communication of the quality of a model fit along with metrics of variable importance. The presented method is accurate, robust, and adaptable to a wide range of predictive modeling algorithms. The method is described along with case studies and a usage guide. It is shown how the method can be used to obtain more accurate models for prediction and how this may simultaneously lead to altered inferences and conclusions about the impact of potential drivers within a system.

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Thu, 27 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/i07
SemiMarkov: An R Package for Parametric Estimation in Multi-State Semi-Markov Models http://www.jstatsoft.org/v66/i06/paper Vol. 66, Issue 6, Aug 2015

Abstract:

Multi-state models provide a relevant tool for studying the observations of a continuous-time process at arbitrary times. Markov models are often considered even if semi-Markov are better adapted in various situations. Such models are still not frequently applied mainly due to lack of available software. We have developed the R package SemiMarkov to fit homogeneous semi-Markov models to longitudinal data. The package performs maximum likelihood estimation in a parametric framework where the distributions of the sojourn times can be chosen between exponential, Weibull or exponentiated Weibull. The package computes and displays the hazard rates of sojourn times and the hazard rates of the semi-Markov process. The effects of covariates can be studied with a Cox proportional hazards model for the sojourn times distributions. The number of covariates and the distribution of sojourn times can be specified for each possible transition providing a great flexibility in a model’s definition. This article presents parametric semi-Markov models and gives a detailed description of the package together with an application to asthma control.

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Thu, 27 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/i06
A Toolbox for Nonlinear Regression in R: The Package nlstools http://www.jstatsoft.org/v66/i05/paper Vol. 66, Issue 5, Aug 2015

Abstract:

Nonlinear regression models are applied in a broad variety of scientific fields. Various R functions are already dedicated to fitting such models, among which the function nls() has a prominent position. Unlike linear regression fitting of nonlinear models relies on non-trivial assumptions and therefore users are required to carefully ensure and validate the entire modeling. Parameter estimation is carried out using some variant of the least- squares criterion involving an iterative process that ideally leads to the determination of the optimal parameter estimates. Therefore, users need to have a clear understanding of the model and its parameterization in the context of the application and data considered, an a priori idea about plausible values for parameter estimates, knowledge of model diagnostics procedures available for checking crucial assumptions, and, finally, an under- standing of the limitations in the validity of the underlying hypotheses of the fitted model and its implication for the precision of parameter estimates. Current nonlinear regression modules lack dedicated diagnostic functionality. So there is a need to provide users with an extended toolbox of functions enabling a careful evaluation of nonlinear regression fits. To this end, we introduce a unified diagnostic framework with the R package nlstools. In this paper, the various features of the package are presented and exemplified using a worked example from pulmonary medicine.

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Thu, 27 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/i05
Fitting Diffusion Item Response Theory Models for Responses and Response Times Using the R Package diffIRT http://www.jstatsoft.org/v66/i04/paper Vol. 66, Issue 4, Aug 2015

Abstract:

In the psychometric literature, item response theory models have been proposed that explicitly take the decision process underlying the responses of subjects to psychometric test items into account. Application of these models is however hampered by the absence of general and flexible software to fit these models. In this paper, we present diffIRT, an R package that can be used to fit item response theory models that are based on a diffusion process. We discuss parameter estimation and model fit assessment, show the viability of the package in a simulation study, and illustrate the use of the package with two datasets pertaining to extraversion and mental rotation. In addition, we illustrate how the package can be used to fit the traditional diffusion model (as it has been originally developed in experimental psychology) to data.

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Thu, 27 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/i04
Parametric and Nonparametric Sequential Change Detection in R: The cpm Package http://www.jstatsoft.org/v66/i03/paper Vol. 66, Issue 3, Aug 2015

Abstract:

The change point model framework introduced in Hawkins, Qiu, and Kang (2003) and Hawkins and Zamba (2005a) provides an effective and computationally efficient method for detecting multiple mean or variance change points in sequences of Gaussian random variables, when no prior information is available regarding the parameters of the distribution in the various segments. It has since been extended in various ways by Hawkins and Deng (2010), Ross, Tasoulis, and Adams (2011), Ross and Adams (2012) to allow for fully nonparametric change detection in non-Gaussian sequences, when no knowledge is available regarding even the distributional form of the sequence. Another extension comes from Ross and Adams (2011) and Ross (2014) which allows change detection in streams of Bernoulli and Exponential random variables respectively, again when the values of the parameters are unknown.
This paper describes the R package cpm, which provides a fast implementation of all the above change point models in both batch (Phase I) and sequential (Phase II) settings, where the sequences may contain either a single or multiple change points.

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Thu, 27 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/i03
OptGS: An R Package for Finding Near-Optimal Group-Sequential Designs http://www.jstatsoft.org/v66/i02/paper Vol. 66, Issue 2, Aug 2015

Abstract:

A group-sequential clinical trial design is one in which interim analyses of the data are conducted after groups of patients are recruited. After each interim analysis, the trial may stop early if the evidence so far shows the new treatment is particularly effective or ineffective. Such designs are ethical and cost-effective, and so are of great interest in practice. An optimal group-sequential design is one which controls the type-I error rate and power at a specified level, but minimizes the expected sample size of the trial when the true treatment effect is equal to some specified value. Searching for an optimal group- sequential design is a significant computational challenge because of the high number of parameters. In this paper the R package OptGS is described. Package OptGS searches for near-optimal and balanced (i.e., one which balances more than one optimality criterion) group-sequential designs for randomized controlled trials with normally distributed outcomes. Package OptGS uses a two-parameter family of functions to determine the stopping boundaries, which improves the speed of the search process whilst still allow- ing flexibility in the possible shape of stopping boundaries. The resulting package allows optimal designs to be found in a matter of seconds " much faster than a previous approach.

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Wed, 26 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/i02
RARtool: A MATLAB Software Package for Designing Response-Adaptive Randomized Clinical Trials with Time-to-Event Outcomes http://www.jstatsoft.org/v66/i01/paper Vol. 66, Issue 1, Aug 2015

Abstract:

Response-adaptive randomization designs are becoming increasingly popular in clinical trial practice. In this paper, we present RARtool, a user interface software developed in MATLAB for designing response-adaptive randomized comparative clinical trials with censored time-to-event outcomes. The RARtool software can compute different types of optimal treatment allocation designs, and it can simulate response-adaptive randomization procedures targeting selected optimal allocations. Through simulations, an investigator can assess design characteristics under a variety of experimental scenarios and select the best procedure for practical implementation. We illustrate the utility of our RARtool software by redesigning a survival trial from the literature.

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Wed, 26 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/i01
MixMAP: An R Package for Mixed Modeling of Meta-Analysis p Values in Genetic Association Studies http://www.jstatsoft.org/v66/c03/paper Vol. 66, Code Snippet 3, Aug 2015

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Wed, 26 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/c03
MLGA: A SAS Macro to Compute Maximum Likelihood Estimators via Genetic Algorithms http://www.jstatsoft.org/v66/c02/paper Vol. 66, Code Snippet 2, Aug 2015

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Wed, 26 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/c02
Testing Goodness-of-Fit with the Kernel Density Estimator: GoFKernel http://www.jstatsoft.org/v66/c01/paper Vol. 66, Code Snippet 1, Aug 2015

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Wed, 26 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/c01
Circular Statistics in R http://www.jstatsoft.org/v66/b05/paper Vol. 66, Book Review 5, Aug 2015

Circular Statistics in R
Arthur Pewsey, Markus Neuhäuser, Graeme D. Ruxton
Oxford University Press, 2013
ISBN: 978-0-19-967113-7

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Mon, 24 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/b05
R for Cloud Computing http://www.jstatsoft.org/v66/b04/paper Vol. 66, Book Review 4, Aug 2015

R for Cloud Computing
Ajay Ohri
Springer-Verlag, 2014
ISBN: 978-1-4939-1701-3

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Sun, 23 Aug 2015 07:00:00 GMT http://www.jstatsoft.org/v66/b04