Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package

Generalized Additive Models(GAMs)are widely employed in ecological research,serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors.Nevertheless,evaluating the relative importance of predictors with concurvity(analogous to collinearity)on response variables in GAMs remains a challenge.To address this challenge,we developed an R package named gam.hp.gam.hp calculates individual R^(2) values for predictors,based on the concept of'average shared variance',a method previously introduced for multiple regression and canonical analyses.Through these individual R^(2)s,which add up to the overall R^(2),researchers can evaluate the relative importance of each predictor within GAMs.We illustrate the utility of the gam.hp package by evaluating the relative importance of emission sources and meteorological factors in explaining ozone concentration variability in air quality data from London,UK.We believe that the gam.hp package will improve the interpretation of results obtained from GAMs.

STRUCTURE PARAMETERS DESIGN AND PERFORMANCE TEST OF FUEL INJECTION SYSTEM

Based on the numerical simulation analysis, structure parameters of the high pressure fuel pump and common rail as well as flow limiter are designed and the GD-1 high pressure common rail fuel injection system is self-developed. Fuel injection characteristics experiment is performed on the GD-1 system. And double-factor variance analysis is applied to investigate the influence of the rail pressure and injection pulse width on the consistency of fuel injection quantity, thus to test whether the design of structure parameters is sound accordingly. The results of experiment and test show that rail pressure and injection pulse width as well as their mutual-effect have no influence on the injection quantity consistency, which proves that the structure parameters design is successful and performance of GD-1 system is sound.

The Impact of Restricted Measures on Price Discovery in Stock Index Futures:Evidence from CSI 500 Stock Index Futures

This paper compares the impact of restricted measures on CSI 500 stock index futures market and its underlying spot market.It uses vector error correction(VECM)model and common factor analysis method to study the differences between the two markets before and after the restricted measures was implemented.This paper analyzes the price discovery function through three aspects,i.e.,response to new information,price ratio of new information,and price discovery contribution degree of two markets.Based on empirical results,it is clear that group one in the period of April 17th to September 2nd has an obvious price discovery function.However,group two in the period of September 7th to December 31th does not have.The result shows that stock index futures do have price discovery function to some extent.However,due to the impact of restrictive policies,the spot market price contribution may exceed the futures market in some special time periods,which implies that the price discovery function of CSI 500 stock index futures market is not stable.

Comparison of Principal Components Analysis,Independent Components Analysis and Common Components Analysis

The aim of this work is to describe and compare three exploratory chemometrical tools,principal components analysis,independent components analysis and common components analysis,the last one being a modification of the multi-block statistical method known as common components and specific weights analysis.The three methods were applied to a set of data to show the differences and similarities of the results obtained,highlighting their complementarity.

Long-Term Trend of Temperature Derived by Statistical Downscaling Based on EOF Analysis

This study analyzes the ability of statistical downscaling models in simulating the long-term trend of temperature and associated causes at 48 stations in northern China in January and July 1961-2006. The statistical downscaling models are established through multiple stepwise regressions of predictor principal components （PCs）. The predictors in this study include temperature at 850 hPa （T850）, and the combination of geopotential height and temperature at 850 hPa （H850＋T850）. For the combined predictors, Empirical Orthogonal Function （EOF） analysis of the two combined fields is conducted. The modeling results from HadCM3 and ECHAM5 under 20C3M and SERS A1B scenarios are applied to the statistical downscaling models to construct local present and future climate change scenarios for each station, during which the projected EOF analysis and the common EOF analysis are utilized to derive EOFs and PCs from the two general circulation models （GCMs）. The results show that （1） the trend of temperature in July is associated with the first EOF pattern of the two combined fields, not with the EOF pattern of the regional warming; （2） although HadCM3 and ECHAM5 have simulated a false long-term trend of temperature, the statistical downscaling method is able to well reproduce a correct long-term trend of temperature in northern China due to the successful simulation of the trend of main PCs of the GCM predictors; （3） when the two-field combination and the projected EOF analysis are used, temperature change scenarios have a similar seasonal variation to the observed one; and （4） compared with the results of the common EOF analysis, those of the projected EOF analysis have been much more strongly determined by the observed large-scale atmospheric circulation patterns.

glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models

Generalized linear mixed models(GLMMs)have been widely used in contemporary ecology studies.However,determination of the relative importance of collinear predictors(i.e.fixed effects)to response variables is one of the challenges in GLMMs.Here,we developed a novel R package,glmm.hp,to decompose marginal R2^(2)explained by fixed effects in GLMMs.The algorithm of glmm.hp is based on the recently proposed approach‘average shared variance’i.e.used for multivariate analysis.We explained the principle and demonstrated the use of this package by simulated dataset.The output of glmm.hp shows individual marginal R2^(2)s that can be used to evaluate the relative importance of predictors,which sums up to the overall marginal R2^(2).Overall,we believe the glmm.hp package will be helpful in the interpretation of GLMM outcomes.

A COMPREHENSIVE DEA APPROACH FOR THE RESOURCE ALLOCATION PROBLEM BASED ON SCALE ECONOMIES CLASSIFICATION

This paper is concerned with the resource allocation problem based on data envelopment analysis （DEA） which is generally found in practice such as in public services and in production process. In management context, the resource allocation has to achieve the effective-efficient-equality aim and tries to balance the different desires of two management layers： central manager and each sector. In mathematical programming context, to solve the resource allocation asks for introducing many optimization techniques such as multiple-objective programming and goal programming. We construct an algorithm framework by using comprehensive DEA tools including CCR, BCC models, inverse DEA model, the most compromising common weights analysis model, and extra resource allocation algorithm. Returns to scale characteristic is put major place for analyzing DMUs＇ scale economies and used to select DMU candidates before resource allocation. By combining extra resource allocation algorithm with scale economies target, we propose a resource allocation solution, which can achieve the effective-efficient-equality target and also provide information for future resource allocation. Many numerical examples are discussed in this paper, which also verify our work.

An Overview of Genome-Wide Association Studies in Alzheimer＇s Disease

Genome-wide association studies（GWASs）have revealed a plethora of putative susceptibility genes for Alzheimer＇s disease（AD）. With the sole exception of the APOE gene, these AD susceptibility genes have not been unequivocally validated in independent studies. No single novel functional risk genetic variant has been identified. In this review, we evaluate recent GWASs of AD, and discuss their significance, limitations, and challenges in the investigation of the genetic spectrum of AD.