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Understanding why a random variable is actually random has been in the core of Statistics from its beginnings. The generalized Waring regression model for count data explains that inherent variability is given by three possible sources: randomness, liability and proneness. The model extends the negative binomial regression model and it is not included in the family of generalized linear models. In order to avoid that shortcoming, we developed the R package for fitting, describing and validating the model. The version we introduce in this communication provides a new design of the modelling function as well as new methods operating on the associated fitted model objects, so that the new software integrates easily into the computational toolbox for modelling count data in R. The release of a plug-in in order to use the package from the interface R Commander tries to contribute to the spreading of the model among non-advanced users. We illustrate the usage and the possibilities of the software with two examples from the fields of health and sport. [ABSTRACT FROM AUTHOR]

Objective: Nosocomial infection is one of the main causes of morbidity and mortality in patients admitted to hospital. One aim of this study is to determine its intrinsic and extrinsic risk factors. Nosocomial infection also increases the duration of hospital stay. We quantify, in relative terms, the increased duration of the hospital stay when a patient has the infection. Methods: We propose the use of logistic regression models with an asymmetric link to estimate the probability of a patient suffering a nosocomial infection. We use Poisson-Gamma regression models as a multivariate technique to detect the factors that really influence the average hospital stay of infected and noninfected patients. For both models, frequentist and Bayesian estimations were carried out and compared. Results: The models are applied to data from 1039 patients operated on in a Spanish hospital. Length of stay, the existance of a preoperative stay and obesity were found the main risk factors for a nosomial infection. The existence of a nosocomial infection multiplies the length of stay in the hospital by a factor of 2.87. Conclusion: The results show that the asymmetric logit improves the predictive capacity of conventional logistic regressions [ABSTRACT FROM AUTHOR]

In this paper we present a family of Pearson's discrete distributions which are generated by the hypergeometric function p+1Fp, an univariate extension of the Gaussian hypergeometric function. It allows us to generalize the study of this type of distributions, whatever the order of the hypergeometric function which is considered. We study the properties that they present, like a recurrence relation that the moments verify, from which we can use the moment's method to estimate the parameters. Also we have obtained a summation result through which we can calculate the probability mass function and the moments of a wide class of distributions. [ABSTRACT FROM AUTHOR]