Using historical data about previous outcomes, the logistic regression algorithm will sort students into "accept" or "reject" categories. Logistic regression is also referred to as binomial logistic regression o
We monitored the survival of nests located in agricultural landscapes and used a binomial logistic regression with main effects for data‐loggers and parasite‐accepting or ‐rejecting status and their interaction. We also compared maximum likelihood–derived DSR for differences in estimated rates, ...
To address this, we developed a paradigm where, in addition to the opening that was either too small or large enough to pass through, we offered an alternative solution (i.e.: detouring the obstacle), to solve the problem (i.e., reach the reward on the other side of the fence). Bas...
To analyze the influence of children age, mixed logistic regression was conducted in R including a random intercept (note that logistic regressions were preregistered but in hindsight the random intercept is necessary to account for dependency in the data54). Results showed a main effect for age ...
We calculated general additive mixed models (GAMMs) with binomial distribution using the function gam ('mgcv' R package (Wood, 2011, Wood, 2017)) to analyse the spatial behaviour and habitat selection of P. auritus after emergence. We run the models for Doberlug-Kirchhain and Briesen ...
🐛 Describe the bug torch-2.4.1, numpy-2.0.0 (same error with 2.0.1) errors out when trying to torch.compile np.random.uniform() but OK in numpy-1.x (verified with numpy-1.26.0). Minimal repro script: import torch import numpy as np # min...
However I am struggling to figure out how to interpret the coefficients of a negative binomial regression in terms of SD. I have normalized all my predictors, but not my output (a count variable). I would like to know how would be the interpretation of my betas in this case. Thank you...
regression methods for count data, where the response variables are non-negative integers (Cameron & Trivedi,2013). This has been typically modeled using Poisson, geometric, or negative binomial distributions (Coxe et al.,2009; Heinen,2003). The above-mentioned models fall under the generalized ...
When doing regression analysis, do we need to consider outliers? Why or why not? A baby formula production process has a mean of 16.1 ounces and a standard deviation of 0.4 ounces. The distribution of output is normal. If we take a sample of 4 units, what is the chance that the mean...
A Poisson or similar count model (quasi-Poisson, negative binomial) makes an assumption about the association between the value of a count and the variance in the count. That won't necessarily hold for this type of ranking data. It's quite possible to perform an ordinal regression with a ...