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You defined C as an ordered variable, which it is, so logistf automatically converts C to an orthogonal polynomial representation, which makes things harder to interpret. Why does R do this? Orthogonal polynomial encoding is a form of trend analysis in that it is looking for the linear, quadratic and cubic trends in the effect of a categorical variable. Here ...


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The result shows only two coefficients for education because glm converts factors (e.g., your education variable) into indicator variables for each level. Since having all theee indicators would be redundant (because if you know the education is not medium or high, it must be low) you only see two, with the third being implicit. When you write y = -2.35 + 0....


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The predictor is categorical (in fact ordinal) with three levels. Since you are representing it with a linear and quadratic term (I suppose), it matters which numerical values you have used to represent it. Three parameters gives a saturated model, and the intercept is an intrinsic part of that description, so it does not really make much sense to look at ...


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Marginal distributions are dependent only on that subset of data without reference to any other. Conditional distributions are influenced by other variables. Copulas create a conditional distribution between variables (e.g. through rank correlation) but do not determine the local marginal distribution of individual variables. The marginal distribution of ...


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Definitely not! The name (I assume) comes from the fact that it is the copula of the multivariate Gaussian distribution. Feel free to use other marginals. I’d encourage you to try some simulations where you have the same Gaussian copula with high correlation while you vary the marginal distributions.


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This question is about interpreting main effects and interactions. The principle is that: main effects that are not involved in an interaction can be interpreted as the association between a 1 unit change in that variable and the outcome, leaving all other fixed effects constant. main effects that are involved in an interaction can be intepreted as the ...


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It is hard to answer this precisely without seeing what you actually typed in Stata (both your logit specification and your margins command, and do note the correct spelling). From the verbal description, it sounds like you are taking the sample used in the estimation of the logit, predicting Pr(Seizure) as if everyone in that sample had a cancer values of ...


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It is best to avoid using percentages as the dependent variable if at all possible, especially if you intend to use the denominator of the percentage as an independent variable. This will introduce sever bias due to mathematical coupling. Another pitfall is to use variables that are rates which are divided by the same denominator for a similar reason. A ...


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It is unclear from your question how these models relate to one another. However, the way you perform a statistical test for equivalence of models is to pose them as nested models and then perform a statistical test to see whether the additional terms in the more complex model are "zero". For example, suppose you have two linear models like this: ...


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My question is that does the model ranking change if I add another random effect (like animals) in the model? This random effect will be the same for all different models. It might, because the estimation procedures are different In mixed effects models. However this should not deter you from using mixed models. They are often the best solution when you ...


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If you want your sample to be representative of the population in terms of the distribution of college membership, then stratified random sampling is the right tool. To achieve a representative sample, sample sizes should be proportional to the fraction of the population which belongs to each school. So, if 75% of students are in the business school and 25% ...


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That ggplot has little to do with the regression model you fit. To get the plot to correspond to your regression model, you need to enter method = "lm" in the call to geom_smooth(). That will produce a straight line that corresponds to the regression you fit. It's also a good idea to have the plot that you made, i.e., with the smoothed fit line, ...


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I am assuming that you copied this from some statistical package. I hope someone finds it clearer, but here is my understanding: The first table is a test on the significance of the whole categorical variable; i.e. it breaks it down to n-1 dummies, where n is the number of possible outcomes and it shows you the overall result indicating whether the inclusion ...


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