Background With progress on both the theoretical and the computational fronts the use of spline modelling has become an established tool in statistical regression analysis. An important issue in spline modelling is the availability of user friendly, well documented software packages. Following the idea of the STRengthening Analytical Thinking for Observational Studies initiative to provide users with guidance documents on the application of statistical methods in observational research, the aim of this article is to provide an overview of the most widely used spline-based techniques and their implementation in R. Methods In this work, we focus on the R Language for Statistical Computing which has become a hugely popular statistics software. We identified a set of packages that include functions for spline modelling within a regression framework. Using simulated and real data we provide an introduction to spline modelling and an overview of the most popular spline functions. Results We present a series of simple scenarios of univariate data, where different basis functions are used to identify the correct functional form of an independent variable. Even in simple data, using routines from different packages would lead to different results. Conclusions This work illustrate challenges that an analyst faces when working with data. Most differences can be attributed to the choice of hyper-parameters rather than the basis used. In fact an experienced user will know how to obtain a reasonable outcome, regardless of the type of spline used. However, many analysts do not have sufficient knowledge to use these powerful tools adequately and will need more guidance. Electronic supplementary material The online version of this article (10.1186/s12874-019-0666-3) contains supplementary material, which is available to authorized users.
How to select variables and identify functional forms for continuous variables is a key concern when creating a multivariable model. Ad hoc 'traditional' approaches to variable selection have been in use for at least 50 years. Similarly, methods for determining functional forms for continuous variables were first suggested many years ago. More recently, many alternative approaches to address these two challenges have been proposed, but knowledge of their properties and meaningful comparisons between them are scarce. To define a state-of-the-art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge many outstanding issues in multivariable modelling remain. Our main aims are to identify and illustrate such gaps in the literature and present them at a moderate technical level to the wide community of practitioners, researchers and students of statistics.We briefly discuss general issues in building descriptive regression models, strategies for variable selection, different ways of choosing functional forms for continuous variables, and methods for combining the selection of variables and functions. We discuss two examples, taken from the medical literature, to illustrate problems in the practice of modelling.Our overview revealed that there is not yet enough evidence on which to base recommendations for the selection of variables and functional forms in multivariable analysis. Such evidence may come from comparisons between alternative methods. In particular, we highlight seven important topics that require further investigation and make suggestions for the direction of further research.
BackgroundMicroarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature’s relevance to a classification task.ResultsWe apply POS, along‐with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance.ConclusionsA novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along‐with a novel gene score are exploited to produce the selected subset of genes.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-274) contains supplementary material, which is available to authorized users.
Combining multiple classifiers, known as ensemble methods, can give substantial improvement in prediction performance of learning algorithms especially in the presence of non-informative features in the data sets. We propose an ensemble of subset of kNN classifiers, ESkNN, for classification task in two steps. Firstly, we choose classifiers based upon their individual performance using the out-of-sample accuracy. The selected classifiers are then combined sequentially starting from the best model and assessed for collective performance on a validation data set. We use bench mark data sets with their original and some added non-informative features for the evaluation of our method. The results are compared with usual kNN, bagged kNN, random kNN, multiple feature subset method, random forest and support vector machines. Our experimental comparisons on benchmark classification problems and simulated data sets reveal that the proposed ensemble gives better classification performance than the usual kNN and its ensembles, and performs comparable to random forest and support vector machines.
A total of 3,782 performance results for male and female weightlifters, ages 14–30 from 123 countries, from Youth, Junior, and Senior World Championships and Olympic Games 2013–2017 were used to estimate the age at peak performance in Olympic weightlifting and quantify performance development from adolescence to adulthood. The age at peak performance was estimated for men and women globally and for different geographic regions. Overall, male and female weightlifters achieve their peak performance in weightlifting at similar ages. The median peak age is 26.0 years (95% CI: 24.9, 27.1) for men and 25.0 years (95% CI: 23.9, 27.4) for women, at the 90th percentile of performances. The median peak age was 26.3 years for men (95% CI: 24.5, 29.6) and 26.4 years for women (95% CI: 24.5, 29.6), at the 50th percentile. It is a novel finding that the age at peak performance varies for male and female athletes from different geographic regions (Western Europe, Eastern Europe, Middle East, Far East, North- and South America). For some regions men reach peak performance at a younger age than women, while this relationship is reversed for other regions. A possible explanation could be that socio-economic factors influence the pool of available athletes and thus may under- or overestimate the true peak age. Unlike in track and field where the discipline might determine specific body types, weightlifters at all ages compete in body weight classes, enabling us to compare performance levels and annual rate of change for athletes of different body mass. We quantified increases in performance in Olympic weightlifting for male and female adolescents. Sex-specific differences arise during puberty, boys outperform girls, and there is a rapid increase in their performance levels before the further growth slows down. The largest annual rate of increase in the total weight lifted was achieved between 16 and 17 years of age for both sexes with lower body mass and between 21 and 22 years with higher body mass. Such new information may help to establish progression trajectories for young athletes.
The INTERNATIONAL WEIGHTLIFTING FEDERATION MASTERS COMMITTEE hereinafter called the IWF Masters is a voluntary committee formed at the 1989 World Masters Games in Aalborg, Denmark, and granted the status of being a committee of the IWF circa 1994. 1.1.2The IWF Masters is responsible for the running of an annual IWF Masters Weightlifting Championship. This includes championships organised by the IWF Masters within the structure of a World Masters Games. The IWF have bestowed the IWF Masters the privilege of being the Governing Body for all Masters weightlifting (2015). IWF MASTERS EMBLEM AND FLAG IWF MASTERS EMBLEM 2.1.1The IWF Masters emblem consists of the following elements. A terrestrial globe with its meridians and parallels. Impressed on the globe are weightlifting figure(s). The emblem surround depicts IWF Masters weightlifting. THE IWF MASTERS FLAG 2.2.1The flag carries the image of the emblem. IWF MASTERS AUTHORISATION 2.3.1The IWF Masters emblem must not be used without the permission of the IWF Masters Committee. 2.3.2The IWF Masters emblem must not be fabricated (badges, medals, etc) without the permission of the IWF Masters Committee. GENERAL PROVISIONS 3.1.The IWF Masters is recognised as an "age committee" of the IWF. 3.2.The IWF Masters follows the IWF Constitution, Anti Doping Policy and the Technical Rules and Regulations and adds supplementary rules and regulations for Masters WL that are not contradictive. 3.3.The IWF Master forbids political and religious discussions. 3.4.The IWF Masters supports a policy of peace, understanding, and friendship. 3.5.The IWF Masters does not distinguish between continents, countries, or individuals for reasons of race, color, gender, religion, or politics. 3.6.The IWF Masters recognises only one Masters representative body from each country. This official Masters body must be officially recognized by a National Weightlifting Federation or approved by a National Olympic Committee.
Background Olympic weightlifting requires technical skills, explosive power, strength, and coordination. Weightlifters can be competitive within a range of morphological characteristics due to competition body weight classes. To date no studies have examined when sex differences arise in weightlifting and the impact of body mass on performances at different ages. Objectives To examine when sex-related differences emerge, to quantify the influence of body mass on performances at different ages, and to estimate the age at peak performance. Methods Competitions results from USA Weightlifting National Championships, Youth, Junior, and Senior from 2014 to 2019 were collected for weightlifters aged 6 to 30. Results At age 10 the median total weight lifted was 51kg and 54kg, respectively, for girls and boys. From age 10 to 12 a gender gap emerges with a sex difference of 11.7% at age 14 at 55kg body mass. At age 25 the sex-related performance difference is smaller for lighter athletes (23.6% at 69kg body mass) and larger for heavier athletes (29.9% at 81kg body mass). The median peak age for men is 26.5 years (95% CI: 25.7, 27.3) and for women 25.9 years (95% CI: 24.7, 27.3). Conclusion We quantified the impact of body weight and age and sex differences for youth and young adults, ages 6 to 30 years old, participating in national level Olympic weightlifting competitions in the United States. Body weight at younger ages has less impact on performance compared to older ages, and boys and girls perform similarly. When reaching the ages typically associated with the onset of puberty, boys' performances rapidly increase and the gap
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