A spatial, climate-determined risk rating for Scleroderris disease of pines in Ontario

1998 ◽  
Vol 28 (9) ◽  
pp. 1398-1404 ◽  
Author(s):  
L A Venier ◽  
A A Hopkin ◽  
D W McKenney ◽  
Y. Wang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Classification Accuracy ◽  
Logistic Regression Model ◽  
Distribution Data ◽  
Parameter Estimates ◽  
Final Model ◽  
Probability Of Occurrence ◽  
Model Classification ◽  
Historical Distribution

We used historical distribution data of Scleroderris disease (caused by the fungus Gremmeniella abietina var. abietina (Lagerb.) Morelet) in Ontario to model its probability of occurrence as a function of climate factors. A logistic regression model of the probability of occurrence as a function of the mean temperature of the coldest quarter and the precipitation of the coldest quarter was a very good fit. The concordance (index of classification accuracy) of the model was 84%. We subsampled the data repeatedly, generated new parameter estimates, and tested the predictions against data not included in the model. Classification accuracy was similar for each subsample model; therefore, we concluded that the final model is stable. Gridded estimates of the climate variables were used to spatially extend the two-variable logistic regression model and produce a probability of occurrence map for Scleroderris disease across Ontario. The predicted map of probability of occurrence fits well with the map of the observed locations of the disease. These results lend credence to previous work that suggests that distribution of Scleroderris disease is strongly influenced by climate. The classification results also suggest that this model is a useful tool for assessing the risk of Scleroderris disease throughout Ontario.

scholarly journals Classifying Oryza sativa accessions into Indica and Japonica using logistic regression model with phenotypic data

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7259
Author(s):  
Bongsong Kim
Keyword(s):  
Logistic Regression ◽  
Oryza Sativa ◽  
Regression Model ◽  
Classification Accuracy ◽  
Logistic Regression Model ◽  
Computational Method ◽  
Phenotypic Data ◽  
Separation Power

In Oryza sativa, indica and japonica are pivotal subpopulations, and other subpopulations such as aus and aromatic are considered to be derived from indica or japonica. In this regard, Oryza sativa accessions are frequently viewed from the indica/japonica perspective. This study introduces a computational method for indica/japonica classification by applying phenotypic variables to the logistic regression model (LRM). The population used in this study included 413 Oryza sativa accessions, of which 280 accessions were indica or japonica. Out of 24 phenotypic variables, a set of seven phenotypic variables was identified to collectively generate the fully accurate indica/japonica separation power of the LRM. The resulting parameters were used to define the customized LRM. Given the 280 indica/japonica accessions, the classification accuracy of the customized LRM along with the set of seven phenotypic variables was estimated by 100 iterations of ten-fold cross-validations. As a result, the classification accuracy of 100% was achieved. This suggests that the LRM can be an effective tool to analyze the indica/japonica classification with phenotypic variables in Oryza sativa.

scholarly journals UTILIZAÇÃO DE TÉCNICAS DE REGRESSÃO LOGÍSTICA NA AVALIAÇÃO DE VAZÕES MÁXIMAS DO RIO DOCE, NO MUNICÍPIO DE COLATINA, ES, BRASIL

2017 ◽  
Vol 39 (3) ◽  
pp. 526
Author(s):  
Wanderson De Paula Pinto ◽  
Gemael Barbosa Lima ◽  
Edson Zambon Monte ◽  
Claudinei Antonio Montebeller
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logit Model ◽  
Flood Control ◽  
Logistic Regression Model ◽  
The State ◽  
Maximum Rainfall ◽  
Probability Of Occurrence ◽  
Flood Warning ◽  
Minas Gerais State

This research aimed to evaluate the impacts of maximum rainfall in watershed of the Rio Doce in the probability of flooding alertflow in the municipality of Colatina, ES, Brazil, using Logit model. To this, it were considered the rainfall stations of the locationsof Aimorés, Tumiritinga, Conselheiro Pena and Resplendor in the Minas Gerais state and Baixo Guandu, Itaguaçu, Itarana andColatina in the state of Espírito Santo, beyond fluviometric station in Colatina municipality, during the period from 01/01/1986to 31/12/2014. The results of the logistic regression model shown significant impacts of the maximums rainfall in Tumiritinga,Aimorés, Baixo Guandu and Colatina in the probability of occurrence of episodes of flood warning in Colatina, ES. Moreover, itwas observed that in periods with higher volumes of rain (spring and summer), the chance of occurrence of a warning flow eventin Colatina increased significantly. Lastly, it is hoped that this article can support actions aimed at flood control.

EFFECTS OF DIFFERENT TYPE OF COVARIATES AND SAMPLE SIZE ON PARAMETER ESTIMATION FOR MULTINOMIAL LOGISTIC REGRESSION MODEL

2016 ◽  
Vol 78 (12-3) ◽  
Author(s):  
Hamzah Abdul Hamid ◽  
Yap Bee Wah ◽  
Xian-Jin Xie
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Regression Model ◽  
Sample Size ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Parameter Estimates ◽  
Multinomial Logistic Regression Model ◽  
Continuous Covariates ◽  
Unbiased Estimates

The sample size and distributions of covariate may affect many statistical modeling techniques. This paper investigates the effects of sample size and data distribution on parameter estimates for multinomial logistic regression. A simulation study was conducted for different distributions (symmetric normal, positively skewed, negatively skewed) for the continuous covariates. In addition, we simulate categorical covariates to investigate their effects on parameter estimation for the multinomial logistic regression model. The simulation results show that the effect of skewed and categorical covariate reduces as sample size increases. The parameter estimates for normal distribution covariate apparently are less affected by sample size. For multinomial logistic regression model with a single covariate study, a sample size of at least 300 is required to obtain unbiased estimates when the covariate is positively skewed or is a categorical covariate. A much larger sample size is required when covariates are negatively skewed.

scholarly journals Self-Selecting Robust Logistic Regression Model

2017 ◽  
Vol 6 (3) ◽  
pp. 132 ◽  
Author(s):  
Idelphonse Leandre Tawanou Gbohounme ◽  
Oscar Owino Ngesa ◽  
Jude Eggoh
Keyword(s):  
Logistic Regression ◽  
Statistical Model ◽  
Regression Model ◽  
Binary Data ◽  
Logistic Regression Model ◽  
Parameter Estimates ◽  
Leverage Points ◽  
Parameter Estimations ◽  
Robust Model ◽  
Probability Of Success

Logistic regression model is the most common model used for the analysis of binary data. However, the problem of atypical observations in the data has an unduly effect on the parameter estimates. Many researchers have developed robust statistical model to solve this problem of outliers. Gelman (2004) proposed GRLR, a robust  model by trimming the probability of success in LR. The trimming values in this model were fixed and the user is required to specify this value well in advance. In particular this study developed SsRLR model by allowing the data itself to select the alpha value. We proposed a Restricted LR model to substitute the LR in presence of outliers. We proved that the SsRLR model is the more robust to the presence of leverage points in the data. Parameter estimations is done using a full Bayesian approach implemented in WinBUGS 14 software.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

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