Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslid...Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed,Kodaikkanal,India using binary logistic regression analysis.Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map,which is used to build the spatial model of landslide susceptibility.The model describes the relationship between the dependent variable(presence and absence of landslide) and the independent variables selected for study(predictor variables) by the best fitting function.A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis.An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable.Relief,slope,aspect,plan curvature,profile curvature,land use,soil,topographic wetness index,proximity to roads and proximity to lineaments are taken as independent variables.The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in thestudy area.The model shows that the most significant parameter contributing to landslides is slope.The other significant parameters are profile curvature,soil,road,wetness index and relief.The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.展开更多
On the first anniversary of the implementation of the new regulations of Beijing Municipality on the management of domestic waste,to understand residents’views on the waste classification policy,the project conducted...On the first anniversary of the implementation of the new regulations of Beijing Municipality on the management of domestic waste,to understand residents’views on the waste classification policy,the project conducted relevant investigation of the satisfaction of residents with the domestic waste classification policy in Daxing District of Beijing,China.Based on the analysis of the survey,this study uses the binary logistic regression model to explore the residents’satisfaction with the new domestic waste classification policy in Beijing and its influencing factors.The data from 398 valid questionnaires involve the demographic characteristics of residents,residents’cognition and views on Beijing municipal solid waste classification policy,and residents’satisfaction with Beijing domestic waste classification policy.The data show that the comprehensive satisfaction level of residents with the domestic waste classification policy in Beijing is quite high,up to 84.7%.Among them,the satisfaction level of residents with the details of the classification standards,the allocation of garbage cans,the publicity and supervision of the policy,incentive measures and the implementation process and effect of the policy is very high,exceeding 80%or even more than 90%.Through binary logistic regression analysis,we come to the conclusion that six factors significantly affect residents’satisfaction with Beijing municipal solid waste classification policy,such as residents’monthly income,household daily average domestic waste production,publicity of waste classification policy,supervisors’better understanding of waste classification standards,guidance of waste delivery by community classification supervisors,and convenience of waste classification process.展开更多
The risk factors of high trait anger of juvenile offenders were explored through questionnaire study in a youth correctional facility of Hubei province, China. A total of 1090 juvenile offenders in Hubei province were...The risk factors of high trait anger of juvenile offenders were explored through questionnaire study in a youth correctional facility of Hubei province, China. A total of 1090 juvenile offenders in Hubei province were investigated by self-compiled social-demographic questionnaire, Childhood Trauma Questionnaire（CTQ）, and State-Trait Anger Expression Inventory-Ⅱ（STAXI-Ⅱ）. The risk factors were analyzed by chi-square tests, correlation analysis, and binary logistic regression analysis with SPSS 19.0. A total of 1082 copies of valid questionnaires were collected. High trait anger group（n=316） was defined as those who scored in the upper 27 th percentile of STAXI-Ⅱ trait anger scale（TAS）, and the rest were defined as low trait anger group（n=766）. The risk factors associated with high level of trait anger included： childhood emotional abuse, childhood sexual abuse, step family, frequent drug abuse, and frequent internet using（P〈0.05 or P〈0.01）. Birth sequence, number of sibling, ranking in the family, identity of the main care-taker, the education level of care-taker, educational style of care-taker, family income, relationship between parents, social atmosphere of local area, frequent drinking, and frequent smoking did not predict to high level of trait anger（P〉0.05）. It was suggested that traumatic experience in childhood and unhealthy life style may significantly increase the level of trait anger in adulthood. The risk factors of high trait anger and their effects should be taken into consideration seriously.展开更多
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 ri...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.展开更多
We modeled binary count data with categorical predictors, using logistic regression to develop a statistical method. We found that ANOVA-type analyses often performed unsatisfactorily, even when using different transf...We modeled binary count data with categorical predictors, using logistic regression to develop a statistical method. We found that ANOVA-type analyses often performed unsatisfactorily, even when using different transformations. The logistic transformation of fraction data could be an alternative, but it is not desirable in the statistical sense. We concluded that such methods are not appropriate, especially in cases where the fractions were close to 0 or 1. The major purpose of this paper is to demonstrate that logistic regression with an ANOVA-model like parameterization aids our understanding and provides a somewhat different, but sound, statistical background. We examined a simple real world example to show that we can efficiently test the significance of regression parameters, look for interactions, estimate related confidence intervals, and calculate the difference between the mean values of the referent and experimental subgroups. This paper demonstrates that precise confidence interval estimates can be obtained using the proposed ANOVA-model like approach. The method discussed here can be extended to any type of experimental fraction data analysis, particularly for experimental design.展开更多
通过融合多个核函数,提出一种多核主成分分析(multi-kernel principal component analysis,MKPCA)和二元Logistic回归耦合的诊断方法(MKPCA-Logistic回归模型)诊断冠心病,较好的解决了单一核函数适应性问题。选取第一舒张波高度U_(1)、...通过融合多个核函数,提出一种多核主成分分析(multi-kernel principal component analysis,MKPCA)和二元Logistic回归耦合的诊断方法(MKPCA-Logistic回归模型)诊断冠心病,较好的解决了单一核函数适应性问题。选取第一舒张波高度U_(1)、第三舒张波高度U_(3)、第一收缩波高度D_(1)、第二收缩波高度D_(2)、第三收缩波高度D_(3)、收缩波的波动值±U_(1)等6个影响因子,建立Logistic回归模型以及MKPCA-Logistic回归模型对冠心病进行诊断。利用预测准确率、误判率和成功率曲线(receiver operating characteristic,ROC)对两种模型的预测精度进行检验。结果表明:MKPCA-Logistic回归模型预测患冠心病的正确率为97%,明显高于Logistic回归模型的正确率92.5%。从ROC曲线分析来看,Logistic回归模型的ROC曲线的曲线下面积(AUC)为0.783,MKPCA-Logistic回归模型的AUC为0.874,耦合模型的分类精度更高。展开更多
目的:评价简易无创模型AAR(AST-to-ALT ratio)、APRI(AST-to-platelet ratio index)、SPRI(spleen-to-platelet ratio index)、API(age-platelet index)、ASPRI(age-spleen-to-platelet ratio index)预测乙型肝炎相关肝硬化的临床价值...目的:评价简易无创模型AAR(AST-to-ALT ratio)、APRI(AST-to-platelet ratio index)、SPRI(spleen-to-platelet ratio index)、API(age-platelet index)、ASPRI(age-spleen-to-platelet ratio index)预测乙型肝炎相关肝硬化的临床价值。方法:慢性乙型肝炎170例,其中病理诊断为非肝硬化138例,肝硬化32例。参照原始文献构建预测肝纤维化程度的简易无创模型。统计分析采用SPSS 13.0软件。简易无创模型在非肝硬化与肝硬化患者之间的比较采用两独立样本的t检验。简易无创模型预测肝硬化的评价采用二分类Logistic逐步回归分析。结果:肝硬化患者的平均AAR、SPRI、API、ASPRI显著大于非肝硬化患者(P=0.000,0.009,0.000,0.005),平均APRI相近于非肝硬化患者(P=0.223)。只有AAR和API符合Logistic回归模型纳入自变量标准(P<0.05)。基于简易无创模型建立的Logistic回归模型预测肝硬化的灵敏度、特异度、阳性预测值、阴性预测值、准确度分别为0.22,0.99,0.70,0.84,0.84。结论:AAR和API可能是预测乙型肝炎相关肝硬化较可靠的简易无创模型。基于简易无创模型建立的预测乙型肝炎相关肝硬化的Logistic回归模型有一定的临床实践效能。展开更多
文摘Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed,Kodaikkanal,India using binary logistic regression analysis.Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map,which is used to build the spatial model of landslide susceptibility.The model describes the relationship between the dependent variable(presence and absence of landslide) and the independent variables selected for study(predictor variables) by the best fitting function.A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis.An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable.Relief,slope,aspect,plan curvature,profile curvature,land use,soil,topographic wetness index,proximity to roads and proximity to lineaments are taken as independent variables.The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in thestudy area.The model shows that the most significant parameter contributing to landslides is slope.The other significant parameters are profile curvature,soil,road,wetness index and relief.The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.
基金supported by the National College Students Innovation and Entrepreneurship Training Programs(CN)(Grant Nos.2021J00054&2019J00127)
文摘On the first anniversary of the implementation of the new regulations of Beijing Municipality on the management of domestic waste,to understand residents’views on the waste classification policy,the project conducted relevant investigation of the satisfaction of residents with the domestic waste classification policy in Daxing District of Beijing,China.Based on the analysis of the survey,this study uses the binary logistic regression model to explore the residents’satisfaction with the new domestic waste classification policy in Beijing and its influencing factors.The data from 398 valid questionnaires involve the demographic characteristics of residents,residents’cognition and views on Beijing municipal solid waste classification policy,and residents’satisfaction with Beijing domestic waste classification policy.The data show that the comprehensive satisfaction level of residents with the domestic waste classification policy in Beijing is quite high,up to 84.7%.Among them,the satisfaction level of residents with the details of the classification standards,the allocation of garbage cans,the publicity and supervision of the policy,incentive measures and the implementation process and effect of the policy is very high,exceeding 80%or even more than 90%.Through binary logistic regression analysis,we come to the conclusion that six factors significantly affect residents’satisfaction with Beijing municipal solid waste classification policy,such as residents’monthly income,household daily average domestic waste production,publicity of waste classification policy,supervisors’better understanding of waste classification standards,guidance of waste delivery by community classification supervisors,and convenience of waste classification process.
基金supported by National Natural Science Foundation of China(No.81373022)
文摘The risk factors of high trait anger of juvenile offenders were explored through questionnaire study in a youth correctional facility of Hubei province, China. A total of 1090 juvenile offenders in Hubei province were investigated by self-compiled social-demographic questionnaire, Childhood Trauma Questionnaire（CTQ）, and State-Trait Anger Expression Inventory-Ⅱ（STAXI-Ⅱ）. The risk factors were analyzed by chi-square tests, correlation analysis, and binary logistic regression analysis with SPSS 19.0. A total of 1082 copies of valid questionnaires were collected. High trait anger group（n=316） was defined as those who scored in the upper 27 th percentile of STAXI-Ⅱ trait anger scale（TAS）, and the rest were defined as low trait anger group（n=766）. The risk factors associated with high level of trait anger included： childhood emotional abuse, childhood sexual abuse, step family, frequent drug abuse, and frequent internet using（P〈0.05 or P〈0.01）. Birth sequence, number of sibling, ranking in the family, identity of the main care-taker, the education level of care-taker, educational style of care-taker, family income, relationship between parents, social atmosphere of local area, frequent drinking, and frequent smoking did not predict to high level of trait anger（P〉0.05）. It was suggested that traumatic experience in childhood and unhealthy life style may significantly increase the level of trait anger in adulthood. The risk factors of high trait anger and their effects should be taken into consideration seriously.
基金This paper was financially supported by NSC96-2628-E-366-004-MY2 and NSC96-2628-E-132-001-MY2
文摘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.
文摘We modeled binary count data with categorical predictors, using logistic regression to develop a statistical method. We found that ANOVA-type analyses often performed unsatisfactorily, even when using different transformations. The logistic transformation of fraction data could be an alternative, but it is not desirable in the statistical sense. We concluded that such methods are not appropriate, especially in cases where the fractions were close to 0 or 1. The major purpose of this paper is to demonstrate that logistic regression with an ANOVA-model like parameterization aids our understanding and provides a somewhat different, but sound, statistical background. We examined a simple real world example to show that we can efficiently test the significance of regression parameters, look for interactions, estimate related confidence intervals, and calculate the difference between the mean values of the referent and experimental subgroups. This paper demonstrates that precise confidence interval estimates can be obtained using the proposed ANOVA-model like approach. The method discussed here can be extended to any type of experimental fraction data analysis, particularly for experimental design.
文摘目的:评价简易无创模型AAR(AST-to-ALT ratio)、APRI(AST-to-platelet ratio index)、SPRI(spleen-to-platelet ratio index)、API(age-platelet index)、ASPRI(age-spleen-to-platelet ratio index)预测乙型肝炎相关肝硬化的临床价值。方法:慢性乙型肝炎170例,其中病理诊断为非肝硬化138例,肝硬化32例。参照原始文献构建预测肝纤维化程度的简易无创模型。统计分析采用SPSS 13.0软件。简易无创模型在非肝硬化与肝硬化患者之间的比较采用两独立样本的t检验。简易无创模型预测肝硬化的评价采用二分类Logistic逐步回归分析。结果:肝硬化患者的平均AAR、SPRI、API、ASPRI显著大于非肝硬化患者(P=0.000,0.009,0.000,0.005),平均APRI相近于非肝硬化患者(P=0.223)。只有AAR和API符合Logistic回归模型纳入自变量标准(P<0.05)。基于简易无创模型建立的Logistic回归模型预测肝硬化的灵敏度、特异度、阳性预测值、阴性预测值、准确度分别为0.22,0.99,0.70,0.84,0.84。结论:AAR和API可能是预测乙型肝炎相关肝硬化较可靠的简易无创模型。基于简易无创模型建立的预测乙型肝炎相关肝硬化的Logistic回归模型有一定的临床实践效能。
