A comparison of model choice strategies for logistic regression

Purpose:The purpose of this study is to develop and compare model choice strategies in context of logistic regression.Model choice means the choice of the covariates to be included in the model.Design/methodology/approach:The study is based on Monte Carlo simulations.The methods are compared in terms of three measures of accuracy:specificity and two kinds of sensitivity.A loss function combining sensitivity and specificity is introduced and used for a final comparison.Findings:The choice of method depends on how much the users emphasize sensitivity against specificity.It also depends on the sample size.For a typical logistic regression setting with a moderate sample size and a small to moderate effect size,either BIC,BICc or Lasso seems to be optimal.Research limitations:Numerical simulations cannot cover the whole range of data-generating processes occurring with real-world data.Thus,more simulations are needed.Practical implications:Researchers can refer to these results if they believe that their data-generating process is somewhat similar to some of the scenarios presented in this paper.Alternatively,they could run their own simulations and calculate the loss function.Originality/value:This is a systematic comparison of model choice algorithms and heuristics in context of logistic regression.The distinction between two types of sensitivity and a comparison based on a loss function are methodological novelties.

Utilization of Logistical Regression to the Modified Sine-Gordon Model in the MST Experiment

In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), observed to travel around the torus in Madison Symmetric Torus (MST). The LR analysis is used to utilize the modified Sine-Gordon dynamic equation model to predict with high confidence whether the slinky mode will lock or not lock when compared to the experimentally measured motion of the slinky mode. It is observed that under certain conditions, the slinky mode “locks” at or near the intersection of poloidal and/or toroidal gaps in MST. However, locked mode cease to travel around the torus;while unlocked mode keeps traveling without a change in the energy, making it hard to determine an exact set of conditions to predict locking/unlocking behaviour. The significant key model parameters determined by LR analysis are shown to improve the Sine-Gordon model’s ability to determine the locking/unlocking of magnetohydrodyamic (MHD) modes. The LR analysis of measured variables provides high confidence in anticipating locking versus unlocking of slinky mode proven by relational comparisons between simulations and the experimentally measured motion of the slinky mode in MST.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Predictive Modeling for Analysis of Coronavirus Symptoms Using Logistic Regression

This paper presents a case study on the IPUMS NHIS database,which provides data from censuses and surveys on the health of the U.S.population,including data related to COVID-19.By addressing gaps in previous studies,we propose a machine learning approach to train predictive models for identifying and measuring factors that affect the severity of COVID-19 symptoms.Our experiments focus on four groups of factors:demographic,socio-economic,health condition,and related to COVID-19 vaccination.By analysing the sensitivity of the variables used to train the models and the VEC(variable effect characteristics)analysis on the variable values,we identify and measure importance of various factors that influence the severity of COVID-19 symptoms.

影响结核性胸腔积液并发胸膜增厚的非条件Logistic回归分析

目的探讨影响结核性胸腔积液并发胸膜增厚的相关危险因素。方法回顾性分析2019年1月至2021年3月广州市胸科医院呼吸内科收治的123例结核性胸腔积液患者的临床资料,统计患者胸膜增厚情况,采用单因素及非条件Logistic回归法分析患者胸膜增厚的影响因素。结果胸膜未增厚与胸膜增厚患者性别、年龄、肺结核、胸水分布、用力肺活量(FVC)、第1秒用力呼气容积(FVE1)/FVC、胸水腺苷脱氨酶(ADA)、血清ADA、胸水白细胞、胸水淋巴细胞、胸水中性粒细胞和淋巴细胞比例比较差异无统计学意义;胸膜未增厚与胸膜增厚患者胸水量、胸水吸收时间、FVE1、胸水乳酸脱氢酶(LDH)、血清LDH、胸水蛋白、血清蛋白比较差异有统计学意义(P<0.05)。Logistic回归分析结果显示,胸水量(中大量)、FVE1、胸水LDH、血清LDH、胸水蛋白及血清蛋白是胸膜增厚发生的危险因素(P<0.05)。结论结核性胸腔积液并发胸膜增厚是多因素作用的结果,胸水量、FVE1、胸水吸收时间、胸水LDH、血清LDH、胸水蛋白和血清蛋白与胸膜增厚的发生密切相关,建议临床予以密切监测并积极采取针对性干预措施。

基于Logistic回归的国际时尚品牌销售渠道选择影响因素分析

为了帮助国际时尚品牌合理、科学地选择销售渠道和制定销售策略,以满足不同地区不同类型消费者的需求,提高品牌市场竞争力。首先依据访谈数据确定分析因子,建立影响国际时尚品牌销售渠道选择的多因素分析理论模型,提出可能影响国际品牌销售渠道选择的消费者偏好分析指标体系,从消费者个人特征、心理偏好、品类偏好以及时尚认知4个维度设计问卷并进行调研。运用SPSS软件对问卷回收数据进行录入,并采用Logistic回归分析数据,从而验证模型假设。通过分析结果可知,以上4个维度下的诸多影响因子均对国际品牌销售渠道的选择产生显著影响,并给出了具体的指导策略。品牌商可以根据研究结果制定对应的销售策略,以有效提高销售渠道利用率,最大化提升品牌销售业绩。

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.

Application of a Novel Method for Machine Performance Degradation Assessment Based on Gaussian Mixture Model and Logistic Regression

The currently prevalent machine performance degradation assessment techniques involve estimating a machine＇s current condition based upon the recognition of indications of failure features,which entail complete data collected in different conditions.However,failure data are always hard to acquire,thus making those techniques hard to be applied.In this paper,a novel method which does not need failure history data is introduced.Wavelet packet decomposition（WPD） is used to extract features from raw signals,principal component analysis（PCA） is utilized to reduce feature dimensions,and Gaussian mixture model（GMM） is then applied to approximate the feature space distributions.Single-channel confidence value（SCV） is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of single-channel.Furthermore,multi-channel confidence value（MCV）,which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression（LR） and that the task of decision-level sensor fusion is also completed.Both SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine breakdown.The method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this task.The proposed method has engineering significance for machine performance degradation assessment.

A Review of the Logistic Regression Model with Emphasis on Medical Research

This study explored and reviewed the logistic regression (LR) model, a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, with emphasis on medical research. Thirty seven research articles published between 2000 and 2018 which employed logistic regression as the main statistical tool as well as six text books on logistic regression were reviewed. Logistic regression concepts such as odds, odds ratio, logit transformation, logistic curve, assumption, selecting dependent and independent variables, model fitting, reporting and interpreting were presented. Upon perusing the literature, considerable deficiencies were found in both the use and reporting of LR. For many studies, the ratio of the number of outcome events to predictor variables (events per variable) was sufficiently small to call into question the accuracy of the regression model. Also, most studies did not report on validation analysis, regression diagnostics or goodness-of-fit measures;measures which authenticate the robustness of the LR model. Here, we demonstrate a good example of the application of the LR model using data obtained on a cohort of pregnant women and the factors that influence their decision to opt for caesarean delivery or vaginal birth. It is recommended that researchers should be more rigorous and pay greater attention to guidelines concerning the use and reporting of LR models.

随机森林模型和Logistic回归模型预测非计划再手术发生风险的效能比较

目的比较随机森林模型和Logistic回归模型预测非计划再手术发生风险的效能。方法在手术麻醉系统中筛选一次住院期间申请2次手术的患者信息。提取所有非计划再次手术患者(n=219)作为研究组,对应科室的计划再次手术患者(n=14311)作为对照组。运用随机森林模型和Logistic回归模型建立非计划再手术预测模型。采用受试者工作特征曲线下面积评价两种模型的预测效能。结果(1)Logistic回归分析结果显示,前次术中输血、罹患恶性肿瘤、合并疾病数量、前次手术切口愈合等级、前次手术级别、前次手术时长、前次手术切口类别是非计划再手术发生的影响因素(P<0.05)。Logistic回归预测模型的曲线下面积为0.922,灵敏度、特异度、准确率分别为92.59%、79.11%、79.28%。(2)随机森林模型特征变量的重要性排序结果显示,前次手术切口类别、前次术中输血、前次手术级别、前次手术切口愈合等级、合并疾病数量、罹患恶性肿瘤等变量的重要性更靠前。随机森林预测模型的曲线下面积为0.866,灵敏度、特异度、准确率分别为80.00%、93.33%、86.66%。Logistic回归预测模型曲线下面积大于随机森林预测模型,但差异无统计学意义(P>0.05)。结论综合使用Logistic回归模型和随机森林模型,并将二者分析结果互为补充,可从各个方面预测非计划再次手术的风险因素,能获得更好的预测效能。

Regional Integrated Meteorological Forecasting and Warning Model for Geological Hazards Based on Logistic Regression

Information model is adopted to integrate factors of various geosciences to estimate the susceptibility of geological hazards. Further combining the dynamic rainfall observations, Logistic regression is used for modeling the probabilities of geological hazard occurrences, upon which hierarchical warnings for rainfall-induced geological hazards are produced. The forecasting and warning model takes numerical precipitation forecasts on grid points as its dynamic input, forecasts the probabilities of geological hazard occurrences on the same grid, and translates the results into likelihoods in the form of a 5-level hierarchy. Validation of the model with observational data for the year 2004 shows that 80% of the geological hazards of the year have been identified as ＂likely enough to release warning messages＂. The model can satisfy the requirements of an operational warning system, thus is an effective way to improve the meteorological warnings for geological hazards.

Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed,Gansu Province,China

Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.

基于Logistic回归模型和PCA模型的急性缺血性脑卒中发作影响因素分析

目的应用Logistic回归模型和主成分分析(PCA)模型分析急性缺血性脑卒中(AIS)发作的影响因素。方法从大连医科大学附属第二医院医渡云科研大数据服务器系统提取2001年1月1日至2021年12月31日的数据,将医院病历系统收录的55620例AIS患者纳入病例组,将筛选后的64134例在医院体检中心体检的人群纳入对照组。收集两组的临床资料,分析AIS发作的影响因素。结果多因素分析结果显示,年龄、性别、肌酐、白细胞计数、血红蛋白、红细胞计数、血小板计数、甘油三酯(TG)、低密度脂蛋白胆固醇(LDL-C)、高密度脂蛋白胆固醇(HDL-C)、丙氨酸氨基转移酶(ALT)、总胆固醇(TC)、γ-谷氨酰转移酶(γ-GGT)水平及吸烟史、饮酒史、高血压、糖尿病、心梗、冠心病、动脉粥样硬化为AIS的影响因素(P<0.05);受试者工作特征(ROC)曲线下面积(AUC)为0.927。PCA提取8个主成分,既往病史、血脂水平风险比较大。结论既往病史及血脂水平是AIS发作的主要影响因素,有效控制原发疾病及血脂水平能够更好控制AIS发作。

The assessment of the outliers of logistic regression model and its clinical application

On the basis of the newly developed regression diagnostic analysis, the diagnostic method with the assessment of the outliers of the logistic regression model was set up and it was used to analyze the prognosis of the patients with acute lymphatic leukemia.

Weighted Maximum Likelihood Technique for Logistic Regression

In this paper, a weighted maximum likelihood technique (WMLT) for the logistic regression model is presented. This method depended on a weight function that is continuously adaptable using Mahalanobis distances for predictor variables. Under the model, the asymptotic consistency of the suggested estimator is demonstrated and properties of finite-sample are also investigated via simulation. In simulation studies and real data sets, it is observed that the newly proposed technique demonstrated the greatest performance among all estimators compared.

基于DCE-MRI表现的logistic回归分析模型在乳腺良恶性病变诊断中的应用

目的 分析基于动态对比增强磁共振成像(DCE-M RI)表现的logistic回归分析模型在乳腺良恶性病变诊断中的应用。方法 回顾性分析2021年1月~2023年10月来我院进行乳腺检查患者161例临床资料。其中良性病变60例、恶性病变101例,分别纳入良性组(n=60)及恶性组(n=101)。分析两组DCE-MRI表现差异,进行单因素分析,利用二元Logistic回归分析构建乳腺良恶性病变诊断模型。采用受试者工作特征(ROC)曲线分析乳腺良恶性病变诊断模型的效能。结果 单因素分析显示,良性组与恶性组TIC曲线、BI-RADS分级、早期强化率、边缘形态及病灶大小比较差异有统计学意义(P<0.05);二元Logistic回归分析结果显示, TIC曲线、BI-RADS分级、早期强化率、边缘形态及病灶大小是乳腺良恶性病变危险征像;构建logistic乳腺癌良恶性病变诊断模型Y=-0.633+0.645TIC曲线+2.112×BI-RADS分级+1.142×早期强化率+1.136×边缘形态+1.136×病灶大小;ROC曲线分析显示该模型诊断效能,AUC为0.944,敏感度为83.33%,特异度为85.15%,提示该模型具有较高的诊断效能。结论 基于乳腺病变早期DCE-MRI表现的logistic诊断模型,能够筛选出对乳腺恶性病变鉴别诊断有意义的特征变量,对乳腺良恶性病变具有较高的诊断效能。

基于有序Logistic回归模型的北京高校男生有氧能力

为探索北京高校男生有氧能力的影响因素,通过随机抽样法抽取134名18~25岁北京高校男生,空腹抽静脉血测血液指标,用德国MetaMax 3B系统实时监测气体代谢,通过线性递增方案测得最大摄氧量(maximum oxygen uptake, VO_(2max))相对值。采用Spearman相关、有序Logistic回归等分析方法进行分析处理。结果表明:回归方程中影响北京高校男生有氧能力的因素有体重(M)、心率(heart rate, HR)、每搏输出量(stroke volume, SV)、心室射血时间(ventricular ejection, VET)、血红蛋白(hemoglobin, HGB)。方程模型系数综合检验步(step)、块(block)、模型(model)检验的P均小于0.01;拟合优度检验的-2对数似然值(-2LL)为159.374,Cox&Snell R^(2)为0.331,Nagelkerke R^(2)为0.373;方程预测等级1准确率为45.5%,等级2准确率为100%,等级3准确率为100%,综合为81.8%,说明Logistic回归模型性能良好。Hosmer和Lemeshow检验预测值与观望值无显著性差异(P>0.05)。可见北京高校男生定量负荷心功能、血液指标与有氧能力的多元Logistic回归模型拟合度较好,且HR、SV、VET、HGB是预测北京高校男生有氧能力的重要因素。同时研究中受试者无需运动至极限状态,运动强度大大降低,可以有效避免运动风险的发生,回归模型的检验结果良好,适合在大样本人群中推广。

基于连续比例Logistic回归模型的贝叶斯判别分析

针对传统贝叶斯判别分析方法处理实际问题的局限性,提出一种基于连续比例Logistic回归模型的贝叶斯判别分析方法.首先基于连续比例Logistic回归模型建立半参数密度比模型,通过经验似然法估计模型的参数,并使用贝叶斯定理计算后验概率进行分类预测.然后对比新方法与传统方法的回判正确率,统计模拟表明当总体数据符合正态分布时,2者判别能力相当,否则,提出的新方法能够更好地判别不同的数据特征.最后运用新方法分析真实的数据集,验证了新方法在分类预测中的准确性和稳健性,与传统方法相比,更适用于实际应用中多元分类问题的建模和预测.

基于Logistic回归与决策树模型的老年多重慢病及影响因素分析

目的:应用决策树模型和Logistic回归对老年多重慢病影响因素进行分析,为多重慢病的管理和防控提供依据。方法:采用多阶段分层随机抽样方法于青岛、广州、苏州抽取1273名老年人,进行老年多重慢病的问卷调查,分别建立Logistic回归模型和决策树模型,分析并比较两种分析方法结果的差异性。结果:Logistic回归结果显示年龄、婚姻状况、医疗保险、吸烟是老年多重慢病的影响因素,其中年龄是保护因素(OR<1)。决策树模型显示医疗保险是老年多重慢病的最主要影响因素,其次是吸烟、年龄和婚姻状况。两种模型分析比较结果显示,Logistic回归模型的灵敏度为74.3%,特异度为55.3%;决策树模型的灵敏度为57.2%,特异度为73.3%。结论:研究显示年龄、婚姻状况、医疗保险、吸烟是老年多重慢病的影响因素。结合运用Logistic回归模型和CHAID模型,可以有效筛选老年多重慢病的危险因素,有助于制定针对性措施,加强管理和防控。

基于多模态超声图像特征的Logistic回归模型预测三阴性乳腺癌病灶肿瘤浸润淋巴细胞表达的临床价值

目的探讨基于多模态超声(包括二维超声、剪切波弹性成像、超声造影及自动乳腺全容积成像)图像特征的Logistic回归模型术前预测三阴性乳腺癌(TNBC)病灶肿瘤浸润淋巴细胞(TILs)表达的临床价值。方法选取经病理证实的TNBC女性患者99例,根据TILs表达水平将其分为TILs低表达组41例(TILs表达水平<20%)和TILs高表达组58例(TILs表达水平≥20%),应用二维超声获取病灶形态、方位、边缘、内部回声、后方回声、钙化等特征,剪切波弹性成像检测病灶剪切波速度(SWV),自动乳腺全容积成像获取病灶有无汇聚征、晕环征、导管改变等特征,超声造影获取起始增强时间、增强强度、增强方向、增强模式、局灶性充盈缺损、周围血管征、增强后病变范围等特征。比较两组多模态超声图像特征的差异;应用多因素Logistic回归分析筛选预测TNBC病灶TILs高表达的独立影响因素,并建立回归模型。绘制受试者工作特征(ROC)曲线分析回归模型预测TNBC病灶TILs高表达的诊断效能。结果TIls高表达组二维超声图像特征形态规则、边缘光整、后方回声增强、内部回声不均匀,以及超声造影图像特征高增强、局灶性充盈缺损占比均高于TIls低表达组,差异均有统计学意义(均P<0.05);两组自动乳腺全容积成像特征及SWV比较差异均无统计学意义。多因素Logistic回归分析显示,形态规则、边缘光整、后方回声增强、高增强及局灶性充盈缺损均为预测TNBC病灶TILs高表达的独立影响因素(OR=6.858、3.824、5.909、1.945、6.522,均P<0.05);建立的回归模型为:Logit(P)=-2.989+1.925×形态规则+1.341×边缘光整+1.776×后方回声增强+0.665×高增强+1.875×局灶性充盈缺损;其预测TNBC病灶TILs高表达的ROC曲线下面积为0.772。结论基于多模态超声图像特征的Logistic回归模型对术前预测TNBC病灶TILs表达有一定的临床价值。