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A statistical approach to determine if data are missing at random

There is a useful package called finalfit check here it has a missing_pairs(outcome VAR, explanatory Vars) were you can explore patterns of missingness and decide whether data is MCAR or MAR. It ...
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1 vote

Multiple membership model random effects specification

My response is coming many years after the original post. However, in case anyone finds this post in the future, it is possible to modify the lmer function from the lme4 R package to fit multiple ...
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  • 11
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Why is Listwise Deletion Standard Error Too Small?

Suppose we have $n$ observations on some normal random variable $X_1$ which is fully observed, and we randomly make 50% of it MCAR to form a new variable called $X_2$. We want to estimate the mean and ...
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Calculation for background characteristic of data sets that were imputed by mice and matched by MatchThem

You can view means and standard deviations within each imputed dataset by requesting balance on all the imputed datasets and requesting the means be displayed. The code below does this: ...
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2 votes
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How to handle with NA's when doing glm in R (and not removing entire rows)?

Missing information (NA) in variables is quite tricky to handle. First of all, NA values will be omitted. It is not possible to fit a regression model using NA values, so you have to handle them ...
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  • 136
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Ordinal model comparison: lower deviance but lower accuracy, is this possible?

The deviance is measuring the errors on the ordinal scale. If the truth is a $2$ and you predict $3$, that does not incur as much of a penalty as would a prediction of $4$. However, when you look at ...
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2 votes

How do you choose the imputation technique?

frankly they all look bad. basically you need multiple imputation even if doing prediction (contrary to what https://scikit-learn.org/stable/modules/impute.html#multiple-vs-single-imputation suggests)....
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