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The first model, a logistic regression (GLM), was used as an example of a simple, spatially homogeneous model, where the probability of deforestation is a function of a set of explanatory variables.
In a first approach, the membrane integrity and esterase activity was modeled with the Gompertz model, a logistic model or a log linear model with or without tailing or shoulder.
The accuracy of the approximations is assessed by simulation for three nonlinear regression models with normal errors, a two-population normal model, a logistic model and a Poisson model.
Open image in new window Fig. 7 Spatial pattern of susceptibility for each model: a logistic regression, b probit regression, c the ANN, d RF-original, and e RF-cost sensitive, f depicts ignition probability.
We also fit a type of generalized linear model, a logistic regression classifier, to the DRB1*0101 training data using only the five physical energy components.
As a statistical model a logistic regression on individual (not marginal) changes is used to interpret the individual odds ratios (likelihood-ratio test).
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Outcome values were simulated from a linear model and a logistic model, each derived from the completed UK700 dataset.
The first model was a logistic model that analysed the determinants of grade 5 CKD, used as a proxy for ESRD.
For part one of the applied models a logistic regression analysis was chosen to predict whether or not patients would utilize resources related to the respective costs figure.
The integrated land use change model combines, a logistic regression model, Markov chain, and cellular automata.
In these analyses, the basic model is a logistic regression model with a binary response (y=1 for child death during the reference period, y=0 otherwise).
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