Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models

2021 ◽  
Author(s):  
Catherine H. Feng ◽  
Mary L. Disis ◽  
Chao Cheng ◽  
Lanjing Zhang
Keyword(s):  
Colorectal Cancer ◽  
Logistic Regression ◽  
Feature Selection ◽  
Random Forest ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
Logistic Regression Models ◽  
Selection For

Domestic Violence Reported to the Police: Correlates of Victims’ Reporting Behavior and Support to Legal Proceedings

2016 ◽  
Vol 31 (3) ◽  
pp. 402-415 ◽  
Author(s):  
Rémi Boivin ◽  
Chloé Leclerc
Keyword(s):  
Domestic Violence ◽  
Logistic Regression ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
The Other ◽  
Individual Factors ◽  
Initial Report ◽  
Logistic Regression Models ◽  
Legal Proceedings ◽  
History Of

This article analyzes reported incidents of domestic violence according to the source of the complaint and whether the victim initially supported judicial action against the offender. Almost three quarters of incidents studied were reported by the victim (72%), and a little more than half of victims initially wanted to press charges (55%). Using multinomial logistic regression models, situational and individual factors are used to distinguish 4 incident profiles. Incidents in which the victim made the initial report to the police and wished to press charges are the most distinct and involve partners who were already separated at the time of the incident or had a history of domestic violence. The other profiles also show important differences.

scholarly journals Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing—A Laboratory Approach

2020 ◽  
Vol 12 (8) ◽  
pp. 1325
Author(s):  
Baru Chung ◽  
Jaehyung Yu ◽  
Lei Wang ◽  
Nam Hoon Kim ◽  
Bum Han Lee ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Mineral Composition ◽  
Regression Models ◽  
Spectral Characteristics ◽  
Random Forest Algorithm ◽  
Band Ratio ◽  
Logistic Regression Models ◽  
Spectral Bands ◽  
Absorption Features

This study introduced a detection method for magnesite and associated gangue minerals, including dolomite, calcite, and talc, based on mineralogical, chemical, and hyperspectral analyses using hand samples from thirteen different source locations and Specim hyperspectral short wave infrared (SWIR) hyperspectral images. Band ratio methods and logistic regression models were developed based on the spectral bands selected by the random forest algorithm. The mineralogical analysis revealed the heterogeneity of mineral composition for naturally occurring samples, showing various carbonate and silicate minerals as accessory minerals. The Mg and Ca composition of magnesite and dolomite varied significantly, inferring the mixture of minerals. The spectral characteristics of magnesite and associated gangue minerals showed major absorption features of the target minerals mixed with the absorption features of accessory carbonate minerals and talc affected by mineral composition. The spectral characteristics of magnesite and dolomite showed a systematic shift of the Mg-OH absorption features toward a shorter wavelength with an increased Mg content. The spectral bands identified by the random forest algorithm for detecting magnesite and gangue minerals were mainly associated with spectral features manifested by Mg-OH, CO3, and OH. A two-step band ratio classification method achieved an overall accuracy of 92% and 55.2%. The classification models developed by logistic regression models showed a significantly higher accuracy of 98~99.9% for training samples and 82–99.8% for validation samples. Because the samples were collected from heterogeneous sites all over the world, we believe that the results and the approach to band selection and logistic regression developed in this study can be generalized to other case studies of magnesite exploration.

scholarly journals Releasing the conceptual spring to construct multiplicative reasoning

2020 ◽  
Author(s):  
Karen Zwanch ◽  
Jesse L. M. Wilkins
Keyword(s):  
Logistic Regression ◽  
Educational Research ◽  
Regression Models ◽  
Middle Grades ◽  
Multinomial Logistic Regression ◽  
American Educational Research Association ◽  
Research Association ◽  
Multiplicative Reasoning ◽  
Logistic Regression Models ◽  
Quantitative Verification

Abstract Constructing multiplicative reasoning is critical for students’ learning of mathematics, particularly throughout the middle grades and beyond. Tzur, Xin, Si, Kenney, and Guebert [American Educational Research Association, ERIC No. ED510991, (2010)] conclude that an assimilatory composite unit is a conceptual spring to multiplicative reasoning. This study examines patterns in the percentages of students who construct multiplicative reasoning across the middle grades based on their fluency in operating with composite units. Multinomial logistic regression models indicate that students’ rate of constructing an assimilatory composite unit but not multiplicative reasoning in sixth and seventh grades is significantly greater than that in eighth and ninth grades. Furthermore, the proportion of students who have constructed multiplicative reasoning in sixth and seventh grades is significantly less than the proportion of those who have constructed multiplicative reasoning in eighth and ninth grades. One implication of this is the quantitative verification of Tzur, Xin, Si, Kenney, and Guebert’s (2010) conceptual spring. That is, students who construct assimilatory composite units early in the middle grades are likely to construct multiplicative reasoning; students who do not construct assimilatory composite units early in the middle grades likely do not construct multiplicative reasoning in the middle grades.

Adverse Childhood Experiences and School-Based Victimization and Perpetration

2017 ◽  
Vol 35 (3-4) ◽  
pp. 662-681 ◽  
Author(s):  
Myriam Forster ◽  
Amy L. Gower ◽  
Barbara J. McMorris ◽  
Iris W. Borowsky
Keyword(s):  
Logistic Regression ◽  
School Violence ◽  
Adverse Childhood Experiences ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
Self Report ◽  
Childhood Experiences ◽  
Logistic Regression Models ◽  
School Based ◽  
Adverse Childhood

Retrospective studies using adult self-report data have demonstrated that adverse childhood experiences (ACEs) increase risk of violence perpetration and victimization. However, research examining the associations between adolescent reports of ACE and school violence involvement is sparse. The present study examines the relationship between adolescent reported ACE and multiple types of on-campus violence (bringing a weapon to campus, being threatened with a weapon, bullying, fighting, vandalism) for boys and girls as well as the risk of membership in victim, perpetrator, and victim–perpetrator groups. The analytic sample was comprised of ninth graders who participated in the 2013 Minnesota Student Survey ( n ~ 37,000). Multinomial logistic regression models calculated the risk of membership for victim only, perpetrator only, and victim–perpetrator subgroups, relative to no violence involvement, for students with ACE as compared with those with no ACE. Separate logistic regression models assessed the association between cumulative ACE and school-based violence, adjusting for age, ethnicity, family structure, poverty status, internalizing symptoms, and school district size. Nearly 30% of students were exposed to at least one ACE. Students with ACE represent 19% of no violence, 38% of victim only, 40% of perpetrator only, and 63% of victim–perpetrator groups. There was a strong, graded relationship between ACE and the probability of school-based victimization: physical bullying for boys but not girls, being threatened with a weapon, and theft or property destruction ( ps < .001) and perpetration: bullying and bringing a weapon to campus ( ps < .001), with boys especially vulnerable to the negative effects of cumulative ACE. We recommend that schools systematically screen for ACE, particularly among younger adolescents involved in victimization and perpetration, and develop the infrastructure to increase access to trauma-informed intervention services. Future research priorities and implications are discussed.

scholarly journals Effects of Social Capital on Credit Access of Farming Households In Vietnam

2014 ◽  
Vol 220 ◽  
pp. 02-18
Author(s):  
HOÀI NGUYỄN TRỌNG ◽  
Bảo Trần Quang
Keyword(s):  
Social Capital ◽  
Logistic Regression ◽  
Rural Areas ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
Credit Market ◽  
Influential Factor ◽  
Credit Access ◽  
Logistic Regression Models ◽  
Farming Households

Social capital is considered as an influential factor in economic transactions, including credit access. The research aims at testing relationships between components of social capital and credit access in Vietnam’s rural areas. The testing is conducted with binary logistic and multinomial logistic regression models. The results show that formal social network reduces possibility of getting access to formal credit, and households with wider formal social networks are likelier to belong to the group with access to semi-formal credit than the group with access to formal credit. Such conflicting results may come from specific characteristics of credit market in Vietnam’s rural areas.

Fall Worry Restricts Social Engagement in Older Adults

2019 ◽  
Vol 32 (5-6) ◽  
pp. 422-431 ◽  
Author(s):  
Namkee G. Choi ◽  
Martha L. Bruce ◽  
Diana M. DiNitto ◽  
C. Nathan Marti ◽  
Mark E. Kunik
Keyword(s):  
Older Adults ◽  
Logistic Regression ◽  
Social Engagement ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Health Related ◽  
Reported Health ◽  
Longitudinal Associations

Objective: To examine cross-sectional and longitudinal associations between (a) activity-limiting fall worry (ALW) and (b) self-reported health-related restrictions and social engagement among older adults. Method: The National Health and Aging Trends Study Waves 5 (T1) and 6 (T2) provided data ( n = 6,279). Binary and multinomial logistic regression models were used to examine association of T2 social engagement restrictions with T2 fall worry and association of T1–T2 changes in social engagement restrictions with T1–T2 changes in fall worry. Results: ALW was significantly associated with both informal and formal social engagement restriction at T2. Onset of ALW and continued ALW between T1 and T2 were also significantly associated with newly reported restrictions in both informal and formal social engagement at T2 even controlling for falls incidents and changes in health status and other covariates. Discussion: The findings underscore the importance of reducing fall worry and preventing social disengagement in late life.

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