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You can add a random effect for point, as this is the experimental unit that is repeatedly measured. However, I'm afraid you cannot attribute the differences in the number of birds to either management type or plot, since these coincide. You could proceed by only including a fixed effect for management type under the (strong) assumption that each plot is ...

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So it appears that the result of surgery has been over-simplified down to a 0/1 outcome, meaning that a much larger sample size will be required in order to build a reliable model, e.g., at least a few hundred surgical failures. The choice of a model for binary Y is the binary logistic regression model. Multinomial logistic regression would be used if Y ...

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If I understand this correctly. you are mainly interested in looking for significant differences among Models (columns) in your table. Also, it seems a stretch to assume these data are normal. So, I would suggest a nonparametric Friedman test, using Years as blocks. [In effect we're assuming Years (blocks) differ, and the Friedman procedure will provide no ...

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First, an $R^2 = 0.234$ isn't necessarily bad and an RMSE = $0.935$K isn't necessarily good. What is a good $R^2$ depends on the field of study and RMSE depends on the scale of measurement. Second, the two measure different things. $R^2$ is a measure of how much of the variation in the DV is accounted for by the model. RMSE is a measure of how close the ...

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You could a mixed effects models for an ordinal outcome variable. The two most popular models for ordinal data are the proportional odds model and the continuation ratio model. For the latter you can find a detailed example on how to fit the model and extract the category-specific probabilities in the vignette Mixed Models for Ordinal Data of the ...

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Which algorithms are [negatively affected by class imbalances]? Many are, at least out-of-the-box: notably decision trees, random forest, neural networks, and SVMs. However, this can usually be patched up with something as simple as class weights, so it's hardly a reason not to choose these algorithms. Which are not? For logistic regression it can be ...

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Good question. I like to look at the deviance goodness of fit to assess which model to use. You can see in your Poisson model that your residual deviance is quite large as compared to the degrees of freedom. This results in a rejection of the null for a deviance goodness of fit test. On the other hand, the negative binomial model seems to fit the data ...

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