RSURV: a function to perform relative survival analysis with S-PLUS or R

Relative survival is a method used to estimate net survival using the expected mortality in the general population. This method is frequently used in cancer registries, more particularly with the Esteve et al. regressive proportional hazards model. Recently, extensions of this model have been developed to account for time-dependent covariate and for time-dependent hazards using B-spline functions. We propose a function, RSurv, to take into account these extensions. Written in the R/S language this function has the same structure of the standard Cox function coxph of R and S-PLUS software with the goal to homogenise survival functions and to take advantages of the power of R and S-PLUS software. We also propose a function, plot.RSurv, for plotting relative survival curves and time-dependent hazards ratio. The usage of these functions is exemplified by a study of a breast cancer hospital-based data set.     

Residual analysis in random regressions using SAS and S-PLUS

A program package RRAP: Random Regression Residual Analysis Program using SAS [1] and S-PLUS [2] is available for performing random regression residual analysis. The PROCEDURE MIXED from SAS is used for statistical inference. Both elementary-level and individual-level residuals are used. The S-PLUS programs provide: (1) a transformation to orthogonalize the elementary-level correlated residuals for standard regression residual analyses; and (2) several statistics and plots for checking model assumptions, assessing model fitting and detecting outlying individuals. RRRAP starts with a SAS Macro RRRAPMAC on the data followed by a S-PLUS Program DoRRRAP on a UNIX system.     

Multiple correspondence analysis in S-PLUS

Multiple correspondence analysis (MCA) is a multivariate method for analyzing multidimensional contingency tables. General software procedures to perform MCA are available. Among them SAS Proc CORRESP, SPAD CORMU procedure and the mca function of the MASS library in S-PLUS are probably the most used. However, CORRESP and CORMU output is different from that of mca function. The aim of this short note is showing how to obtain from mca function results compatible with those achieved with SAS or SPAD. A modified code is proposed in order to obtain the same coordinate system computed by SAS and SPAD. Moreover, the computation of the contributions of the levels of the factors to the inertia explained by each axis, the squared cosine of each factor level and the re-evaluation of the inertia explained by each axis have been added in order to improve the interpretations of the results of the decomposition.     

Relative survival analysis in R

Relative survival techniques are used to compare the survival experience in a study cohort with the one expected should they follow the background population mortality rates. The techniques are especially useful when the cause-specific death information is not accurate or not available since they provide a measure of excess mortality in a group of patients with a certain disease. There are several approaches to modeling relative survival, but there is no widely used statistical package that would incorporate the relevant techniques. The existing software was mostly written by the authors of different methods, in different computer languages and with different requirements for the data input, which makes it almost impossible for a user to choose between available models. We describe our R package relsurv that provides functions for easy and flexible fitting of several relative survival regression models.     

利用星载InSAR技术提取镇江地区DEM及其精度分析

InSAR技术是目前获取高精度数字高程模型（DEM）的一种新方法。为了分析InSAR技术提取DEM的精度,首先介绍了美国航天飞机雷达SRTM DEM的精度和数据结构,然后以江苏镇江地区作为试验区,采用ERS1/2卫星影像来提取DEM,并对星载SAR提取的DEM与SRTM 3弧秒分辨率DEM的精度作了比较。 结果表明,利用星载SAR提取的DEM分辨率与SRTM 3弧秒分辨率的DEM相当,能很好地显示出地形起伏（如山脉、沟谷）的纹理特征。进一步的研究还表明,利用InSAR技术提取DEM的精度与SRTM 3 DEM之间存在5米左右的系统误差,并对产生这一系统误差的原因作了详细分析。     

Selecting software reliability growth models and improving their predictive accuracy using historical projects data

During software development two important decisions organizations have to make are: how to allocate testing resources optimally and when the software is ready for release. SRGMs (software reliability growth models) provide empirical basis for evaluating and predicting reliability of software systems. When using SRGMs for the purpose of optimizing testing resource allocation, the model's ability to accurately predict the expected defect inflow profile is useful. For assessing release readiness, the asymptote accuracy is the most important attribute. Although more than hundred models for software reliability have been proposed and evaluated over time, there exists no clear guide on which models should be used for a given software development process or for a given industrial domain.Using defect inflow profiles from large software projects from Ericsson, Volvo Car Corporation and Saab, we evaluate commonly used SRGMs for their ability to provide empirical basis for making these decisions. We also demonstrate that using defect intensity growth rate from earlier projects increases the accuracy of the predictions. Our results show that Logistic and Gompertz models are the most accurate models; we further observe that classifying a given project based on its expected shape of defect inflow help to select the most appropriate model.     

Estimation and model predictive control of non-linear batch processes using linear parameter varying models

In this paper, feedback control is implemented for batch processes using linear models which describe the batch dynamics locally along its optimal trajectory. A Linear Parameter Varying (LPV) model obtained by interpolation between these multiple models is used to emulate the behaviour of the non-linear batch. The interpolation functions and state estimates are computed using a recursive Bayesian technique. The control technique is based on model predictive control (MPC) which is used for regulation and targeting the product specifications at the end of the batch.     

Detection of linear and nonlinear dependencies in time series using the method of surrogate data in S-PLUS

A general implementation of the method of surrogate data in the S programming language for use with the S-PLUS statistical package is presented. We illustrate the application of the S functions to testing hypotheses about a human heart rate time series and demonstrate that there is evidence for both linear and nonlinear dependencies. We expect these S functions will be useful for the application of the method of surrogate data to the analysis of biomedical time series using the S-PLUS statistical software package.     

On predictive accuracy and risk minimization in pairwise label ranking

We study the problem of label ranking, a machine learning task that consists of inducing a mapping from instances to rankings over a finite number of labels. Our learning method, referred to as ranking by pairwise comparison (RPC), first induces pairwise order relations (preferences) from suitable training data, using a natural extension of so-called pairwise classification. A ranking is then derived from a set of such relations by means of a ranking procedure. In this paper, we first elaborate on a key advantage of such a decomposition, namely the fact that it allows the learner to adapt to different loss functions without re-training, by using different ranking procedures on the same predicted order relations. In this regard, we distinguish between two types of errors, called, respectively, ranking error and position error. Focusing on the position error, which has received less attention so far, we then propose a ranking procedure called ranking through iterated choice as well as an efficient pairwise implementation thereof. Apart from a theoretical justification of this procedure, we offer empirical evidence in favor of its superior performance as a risk minimizer for the position error.     

Regression models for grouped survival data: Estimation and sensitivity analysis

In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models.     

Estimation of nearshore wave transmission for submerged breakwaters using a data-driven predictive model

The functional design of submerged breakwaters is still developing, particularly with respect to modelling of the nearshore wave field behind the structure. This paper describes a method for predicting the wave transmission coefficients behind submerged breakwaters using machine learning algorithms. An artificial neural network using the radial-basis function approach has been designed and trained using laboratory experimental data expressed in terms of non-dimensional parameters. A wave transmission coefficient calculator is presented, based on the proposed radial-basis function model. Predictions obtained by the radial-basis function model were verified by experimental measurements for a two dimensional breakwater. Comparisons reveal good agreement with the experimental results and encouraging performance from the proposed model. Applying the proposed neural network model for predictions, guidance is given to appropriately calculate wave transmission coefficient behind two dimensional submerged breakwaters. It is concluded that the proposed predictive model offers potential as a design tool to predict wave transmission coefficients behind submerged breakwaters. A step-by-step procedure for practical applications is outlined in a user-friendly form with the intention of providing a simplified tool for preliminary design purposes. Results demonstrate the model’s potential to be extended to three dimensional, rough, permeable structures.

     

An experimental investigation of the predictive accuracy of induction and regression

As both research efforts and commercial adoption of Expert Systems (ESs) increase, knowledge acquisition continues to engage the attention of many ES developers. Many view the development of automated knowledge acquisition methods as one of the last remaining barriers to more widespread ES adoption and use. Rule induction has been advanced as a promising method of knowledge acquisition for expert systems, both to understand the weighting and importance of decision criteria and to provide a predictive capability for new examples. One important strand of research has naturally begun to compare the performance of rule induction to those of traditional statistical methods, such as multiple regression. In pursuing this subject, we document a comparison of the predictive capabilities of both induction and multiple regression. The study uses a sample of over 400 MBA students, employing their current GPA as the dependent variable or goal and prior (undergraduate) GPA and GMAT scores as independent or causal variables. An extensive comparison of both methods yields conclusions at variance with many previous studies which assert the superiority of the inductive method. Furthermore, the study identifies conditions under which the inductive method may be more effective as a predictor and develops recommendations for a contingency-based approach to knowledge acquisition for ES.     

Estimation of the cloud effective particle radius using MTSAT-1R data

The algorithm used to retrieve the cloud effective particle radius from the 3.7 μm band was adapted to the corresponding channel of the Japanese Advanced Meteorological Imager (JAMI) flown on board the Multi-functional Transport Satellite (MTSAT) geostationary platform. Snapshot comparisons with spatially well-resolved retrievals from the MODerate resolution Imaging Spectrometer (MODIS) instruments flown on the Terra polar platforms show qualitative agreement with MTSAT retrievals. The results of analysing daytime variation from eastern Asia to the northwest Pacific Ocean show that, not only is the effective particle radius smaller in continental clouds than in maritime clouds, but the daytime amplitude of the effective particle radius is also greater in continental clouds than in maritime clouds, where the effective particle radius value is approximately constant.     

Orientation accuracy analysis of multiple satellite networks using epidemic model

Micro-navigation sensors provide position and orientation information for the network of satellites. Each micro-navigation sensor system consists of a global positioning system receiver, a solid-state inertial measurement unit and lasers. To maintain the orientation accuracy of the satellites in the network, an orientation information transfer (OIT) method was developed. Two laser links are established to align a satellite with low orientation accuracy from a satellite with high orientation accuracy. The OIT process is similar to the spread of an epidemic, which has been extensively studied in the epidemiology. Therefore, the information transfer process was analysed by applying a modified epidemic model. The relation between OIT process and the orientation accuracy of the satellites in the network was mathematically investigated. It is shown that the OIT using lasers greatly improves the orientation accuracy of all satellites in the network. Furthermore, the overall orientation accuracy of the satellite network can be improved by increasing the alignment rate or decreasing the decay rate, which are the parameters of the system. The simulation results verified the analysis of the system. The results showed the feasibility of using epidemic theory to analyse orientation accuracy of a satellite network. Finally, this OIT model will allow coordination of relative attitudes between satellites.     

A hybrid approach for improving predictive accuracy of collaborative filtering algorithms

Recommender systems represent a class of personalized systems that aim at predicting a user’s interest on information items available in the application domain, operating upon user-driven ratings on items and/or item features. One of the most widely used recommendation methods is collaborative filtering that exploits the assumption that users who have agreed in the past in their ratings on observed items will eventually agree in the future. Despite the success of recommendation methods and collaborative filtering in particular, in real-world applications they suffer from the insufficient number of available ratings, which significantly affects the accuracy of prediction. In this paper, we propose recommendation approaches that follow the collaborative filtering reasoning and utilize the notion of lifestyle as an effective user characteristic that can group consumers in terms of their behavior as indicated in consumer behavior and marketing theory. Emanating from a basic lifestyle-based recommendation algorithm we incrementally proceed to the development of hybrid recommendation approaches that address certain dimensions of the sparsity problem and empirically evaluate them providing further evidence of their effectiveness.     

Estimation of individual prediction reliability using the local sensitivity analysis

For a given prediction model, some predictions may be reliable while others may be unreliable. The average accuracy of the system cannot provide the reliability estimate for a single particular prediction. The measure of individual prediction reliability can be important information in risk-sensitive applications of machine learning (e.g. medicine, engineering, business). We define empirical measures for estimation of prediction accuracy in regression. Presented measures are based on sensitivity analysis of regression models. They estimate reliability for each individual regression prediction in contrast to the average prediction reliability of the given regression model. We study the empirical sensitivity properties of five regression models (linear regression, locally weighted regression, regression trees, neural networks, and support vector machines) and the relation between reliability measures and distribution of learning examples with prediction errors for all five regression models. We show that the suggested methodology is appropriate only for the three studied models: regression trees, neural networks, and support vector machines, and test the proposed estimates with these three models. The results of our experiments on 48 data sets indicate significant correlations of the proposed measures with the prediction error.     

tdc.msm: an R library for the analysis of multi-state survival data

The aim of this paper is to present an R library, called tdc.msm, developed to analyze multi-state survival data. In this library, the time-dependent regression model and multi-state models are included as two possible approaches for such data. For the multi-state modelling five different models are considered, allowing the user to choose between Markov and semi-Markov property, as well as to use homogeneous or non-homogeneous models. Specifically, the following multi-state models in continuous time were implemented: Cox Markov model; Cox semi-Markov model; homogeneous Markov model; non-homogeneous piecewise model and non-parametric Markov model. This software can be used to fit multi-state models with one initial state (e.g., illness diagnosis), a finite number of intermediate states, representing, for example, a change of treatment, and one absorbing state corresponding to a terminal event of interest. Graphical output includes survival estimates, transition probabilities estimates and smooth log hazard for continuous covariates.     

模拟预测滤波器的设计与分析

设计了一种具有特殊的窄带信号模型预测性能的模拟连续时间滤波器。该滤波器是通过离散时间区域中使用模型传递函数设计的具有有源RC的结构。