All data were statistically analyzed using SPSS 12.0 for Windows (SPSS Institute, Chicago, Illinois, USA). Multiplelogistic regression analysiswas performed to adjust for other predictors of new infarcts during
ROC graphs are useful for visualizing the performance of binary (having two possible outcomes) classifiers. It is a well-known means for evaluating diagnostic tools in medicine, but in general it can be used for evaluation of all kinds of classification algorithms (Fawcett, 2005). The obvious ...
Table 8 Parameter estimation of undernutrition indicators using Multivariate binary logistic regression Full size table Fixed effect model The independent variables that were included in the current study were: the education level of the mother, place of delivery, birth size of children, age of childre...
Transformation of binary outcomes forregression analysis Outliers: Cook's distance Sample (or mean) trimming Windsorizing Curve Fitting: Theoretical fits: logistic, Cauchy, beta, gamma,Rayleigh, Weibull, exponential,negative exponential, log-normal, Pareto, Laplace, and Gompertz distributions ...
15 In this section, we use the index of innovation as the dependent variable in the regression equation, as an alternative to the fourth logistic regression on the binary variables inprod, inproc, inorg, and inmkt.16 Note that attending to RP2, which pointed to the high association between...
GIS-based forest fire susceptibility mapping in Iran: A comparison between evidential belief function and binary logistic regression models. Scand. J. For. Res. 2016, 31, 80–98. [Google Scholar] [CrossRef] Pourtaghi, Z.S.; Pourghasemi, H.R.; Rossi, M. Forest fire susceptibility mapping...
The ROC curve is a convenient and efficient tool used in the study of binary classification problems. It plots the true positive rate against the false positive rate at different cutoff points or thresholds, constructing a monotonically increasing curve. The area under the ROC curve (AUC) serves...