I am trying to fit a multiple linear regression model to my data using the function fitlm(x) and I'm wondering how to go about controlling for some confounding variables in the model, and later the prediction of my dependent variable. Are there different possibilities to do so? ...
Variable and threshold selection to control predictive accuracy in logistic regression. J. R. Statist. Soc. C 63, 657-672.Kuk, A. Y. C., Li, J. and Rush, J. A. (2014). Variable and thresh- old selection to control predictive accuracy in logistic regression. Applied Statistics, DOI:...
For each of these analyses, the alpha level was set at 0.05. Finally, we investigated the relationship between \(\omega\) and weight status on a continuous scale by running a post hoc linear regression model including BMI and BMI2 as orthogonal predictors....
For regression-based control design of the second kind, machine learning is exploited to identify arbitrary nonlinear control laws that minimize the cost function of the system. In this case, it is not necessary to know the model, control law structure, or the optimizing actuation command, and ...
This paper evaluates the performance of four variable selection methods suitable for case-control studies. Two of the methods are logistic regression and the rank transformed version of it which uses the ranks of the explanatory variables in place of the original observations. The third method is ...
Regression Control Method with Stata 回归控制法及Stata应用 School of Economics Shandong University 颜冠鹏 Guanpengyan@mail.sdu.edu.cn Outline 1. Introduction 2. Model 3. Extension 4. The rcm command 5. Examples 1. Introduction Regression control method (RCM) Aka a panel data approach for ...
R2 values represent model fit and are important indicators of the goodness of fit of a linear equation, reflecting the ability of the regression model to explain the variation of the dependent variable. In this model, R2 was 0.6641, indicating the goodness of fit of the model, where 66.41% ...
6.1.1 Basic Model 96 6.1.2 Controlled Variable as Additional State 97 6.1.3 Manipulated Variable as Additional State 98 6.1.4 Kalman Filter 100 6.2 Open-Loop Prediction Module 103 6.3 Steady-State Target Calculation Module 104 6.3.1 Constraints on Steady-State Perturbation Increment 104 ...
3.2. Constructs and sources Within the sustainability consciousness framework, sustainability knowledge and attitude were taken as the independent variables, and sustainability behaviour was regarded as dependent variable. Sustainability consciousness was analyzed through scale given by Gericke et al. (2019),...
The pruned properties included the first 2-nucleotide region of the −10ext motif and the last 3-nucleotides of the Disc motif, which had no discernable effect on TX rate in this dataset. Overall, we found that a ridge regression model with 346 fitted coefficients yielded a convergent ...