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3 votes

Interpreting sub-sample analysis when coefficient signs are opposite

First, I would not divide by quartile of age. If you are interested in age, I'd add it (in years) to the model, maybe add a spline of it, and maybe add interactions with D. Second, I would not try to ...
Peter Flom's user avatar
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0 votes
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What's the justification for comparing two separate models built on subsets of data versus using one model that uses the whole dataset?

Running two models lets you compare the model for the two subsets -- e.g. in your example, control and disease subsets. Also, when you have specifically "control" and "disease" ...
Peter Flom's user avatar
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3 votes

How to interpret a nonsignificant interaction effect with significant main effects?

Your study objective is to assess effects of four drugs on a health outcome with reference to the effect of a control substance on a health outcome. It is not about influence of time on drug or drug ...
DrJerryTAO's user avatar
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7 votes

Frank Harrell's interpretation of interaction in regression results

There are several examples in the help file for the contrast function in the R rms package here. An expansion of this answer is ...
Frank Harrell's user avatar
5 votes

Frank Harrell's interpretation of interaction in regression results

The quoted comment is about interpreting coefficients in a model with interaction terms. The interaction effect is one type of nonlinear effects. Common interaction terms are two-way between two ...
DrJerryTAO's user avatar
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7 votes

Missing Coefficients in Linear Regression with Multiple Categorical Variables in R

The intercept is the predicted level of the dependent variable when all the independent variables are 0 (however that is coded in your data). There are a number of ways to parametrize categorical ...
Peter Flom's user avatar
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9 votes
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Missing Coefficients in Linear Regression with Multiple Categorical Variables in R

You need to define a reference level for each separate categorical variable, which will be absorbed into the intercept. (Specifically, R does this automatically, by using the alphabetically first ...
Stephan Kolassa's user avatar
0 votes

Predicted circular regression curve from a bpnr regression

Thank you for your answer. Below the functions I used. I think I fixed the problem and my formula for the regression curve is correct ? ...
Tanuki's user avatar
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1 vote
Accepted

auto.arima results in R has no intercept

The function auto.arima() has many arguments that you can add. Maybe what you are looking for auto.arima(x, allowmean = TRUE), ...
krauuuus's user avatar
7 votes

How should I interpret categorical with continuous variables in a logistic regression output?

Programming suggestions: Remove the asterisks from the output as this represents bad statistical practice Don’t use long-winded function calls in formulas as this messes up all the later model output ...
Frank Harrell's user avatar
7 votes

How should I interpret categorical with continuous variables in a logistic regression output?

Your first bullet is right for a person with a BMI of 0. Of course, no one has such a BMI. (That's assuming BMI wasn't centered or scaled). In your second and third bullets, you left out "by a ...
Peter Flom's user avatar
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0 votes

how to interpret a generalised linear mixed model with binomial data

Answering just based on the output presented, without any real understanding of the data or model diagnostics: Also assuming the null hypothesis was that the Pre_postdev effect was not a significant ...
R Carnell's user avatar
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