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You have given proper justification in your question. If you want to know the effect of one with the rest held constant, use a main effects model. The principle of marginality states that main effects do not exist in the presence of an interaction, so beware that this is only valid if there is indeed only a negligible interaction effect. Note that the ...


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From the estimation perspective, pweights is internally used the same way as any other weight. in OLS: $$ min_{\beta} = \sum{(y-\beta X)^2 * w} $$ The only difference with other methods is during the estimation of standard errors. When you use pweights, is like requesting robust standard errors. (sandwich formula). For further details on how exactly weights ...


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If you want $\gamma$ to be exactly 0.5 you must add it as a parameter with assigned coefficient. Leaving it as an unspecified intercept will estimate the coefficient instead. Depending on the features and size of the data you're working with the model may find a value close to 0.5, but it will have extra variance compared to using a fixed coefficient. (Of ...


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You are correct that your Model 2 makes the most sense if you wish to standardize your continuous predictor C. The confusion comes from what the intercept and the coefficient for the binary predictor B mean in Model 0 versus Model 2. I assume that Stata is using treatment coding of the predictors, and that by standardizing C you mean subtracting its mean and ...


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One of the confusing things about ordinal logistic regression is that there are several different ways to model the odds ratio between the ordinal categories. I found Frank Harrell's book on regression modeling really helpful to understand this as well as this paper: https://faculty.washington.edu/heagerty/Courses/b571/homework/Ananth-Kleinbaum-1997.pdf The ...


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There is no inherent problem in using a proportion as a predictor in a survival model, if that is what your understanding of the subject matter indicates. From your description, it seems that keeping track of the total number of "balls," in addition to the fractions of individual "colors," might help. If you know the number of "balls&...


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I think the answer would be "yes" in this case, and the reason I would answer in the affirmative is because it is reminiscent of the Voltage Meter story. In short (paraphrasing from Computer Aged Statistical Inference), an engineer measures the voltages on a batch of 12 tubes using a voltmeter that is normally calibrated $$ x \sim \mathcal{N}(\mu,1)...


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First off, you are right that family="compois" will by default use a log link as is standard in Poisson regression. You can learn this from the output of ?family_glmmTMB: compois(link = "log") which tells you the default link is "log". Thus, you can do the standard exponential transformation of parameter estimates to obtain the ...


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