We follow Wang [2] approach and apply logistic regression [3] on the StockTwits dataset.
This paper presents a new assessment approach which combines the use of a multivariate adaptive regression splines (MARS) approach and the logistic regression (LR) method.
The case-cohort approach and the logistic approach using our proposed control definition (i) gave similar precision (looking at the empirical standard deviations).
Family-based association analysis was performed using a case pseudo-control approacase pseudo-controllogistic regression bapproachpandntal genotypes.
A time-matched case-control study was used as a study design approach, and conditional logistic regression as the analytical method.
For scenario 1, type I errors amount to 0.414 and 0.3 for the direct approach and the logistic regression, respectively, whereas type II errors amount to 0.014 and 0.020 (cf. Fig S9, S10 and S11 for detailed distributions of scenario probabilities).
Here, we apply the classical case control approach and use logistic regression to control for confounding factors, to approximate the relative risk of PCB exposure on this population of cetaceans and discuss the validity of using epidemiologic study methods to determine potential population-level effects of contaminant exposure in wildlife.
For instance, J48 and C45 are decision tree-based algorithms, while NaiveBayes and BayesNet are Bayesian approaches and SimpleLogistic, Logistic, MaxEnt and MaxEntL1 are logistic regression models.
Given the nature of the research question and that the NIS database has a sample design, Drs. Guller and Pietrobon opted for an approach involving multiple and logistic regression models while adjusting for sampling weights, strata, and clusters.
Chun et al. used this approach and compared five logistic regression (LR) based nomograms with other LR based models, namely look up table, classification and regression tree, artificial neural networks and risk group stratification [ 32].
