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Dec
27
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Dec
26
comment How can I pool bootstrapped p-values across multiply imputed data sets?
@tomka. I would certainly do the same as D L Dahly, and study the within and between imputation distributions. In order to integrate both types of distributions, we need to combine them in some way. My suggestion is to simply mix them.
Dec
20
answered How can I pool bootstrapped p-values across multiply imputed data sets?
Oct
23
revised How to get pooled p-values on tests done in multiple imputed datasets?
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Oct
23
answered How to get pooled p-values on tests done in multiple imputed datasets?
Oct
23
comment Analyzing multiply imupted data from Amelia in R: Why do results from zelig and mice differ?
It seems that as.mids2() does not produce a proper mids-object. It returns 5 imputations instead of 15.
Jul
15
answered Learn a joint distribution from incomplete samples
Jul
4
comment How to combine multiple imputed datasets?
If you don't know where the missing data are, you'll need to backcalculate them from the imputed data. This will incorrectly classify points as observed if, by happenstance, all imputations for that cell are identical across the m data sets. As a consequence, the diagnostics may incorrectly label imputed points as observed points (in mice terminology: some red points are incorrectly plotted as blue points). However, this does not affect the validity of the statistical inferences. So, with some extra effort, you can.
Jul
4
comment How to combine multiple imputed datasets?
Just pool! There is no theory that allows us to do this. But there is no theory that forbids this either.
Jul
4
comment How to combine multiple imputed datasets?
Yes, you can, but you need to transform the multiply-imputed data into a mids object in order to use the standard mice post-imputation functions for repeated analyses, diagnostics and pooling. The next version of mice (2.18) will include an as.mids function that does this, but it requires the original data to be present. It won't (yet) handle the case where we don't know where the missing data are.
Jul
4
comment How to combine multiple imputed datasets?
The statement finaldata <- complete(data, "long") in [mice][1] does the same. It can also produce other shapes, e.g. a broad matrix or repeated matrix. [1]: cran.r-project.org/web/packages/mice/index.html "mice"
Jul
4
comment How to combine multiple imputed datasets?
Averaging data is bad because it inflates correlations. The real question is why you think you need a single imputed data set. Everything that you can do with a single data set, you can do on a multiply-imputed data set.
Jul
3
answered Selecting cases for analysis based on multiply imputed values
Jul
3
answered Means of determining monotonicity of missing pattern other than eyeballing? And how monotone is monotone for the purpose of multiple imputation?
May
27
answered Multiple imputation for variables used to calculate regression weights
Apr
27
comment Estimation with transformations of variables after multiple imputation
Yes, I would also be worried if imputation changes the sign, especially if the effect is significant. So it seems that you need to drill down a bit, and study if and how the imputed values differ from the observed values. For example, plot each x against s, and give the observed and imputed data different colors. Passive imputation means that you calculate the summary variable within the algorithm, so you can use it to impute other incomplete variables.
Apr
27
comment Why does MICE fail to impute multilevel data with 2l.norm and 2l.pan?
Now addressed in mice 2.16 (will stop with an error message).
Apr
27
comment Diagnosing why MICE is crashing R when attempting to impute multilevel data
As Hadley suggested, the problem was in the compiled code, in this case the pan() function. I have added tighter error checking, so the issue is solved in mice 2.16.
Apr
21
comment Imputing a missing variable based on common variables with another data set
You have changed the description of your approach I, and my comment no longer applies.
Apr
15
comment Bootstrapped confidence intervals for the parameters of a linear model applied to multiply imputed data
Thanks for the clarification. In the previous version I missed the step where you redraw the multiply-imputed data for every replication. You will probably need large m to incorporate adequate between-imputation variability. If I understand correctly you will have the following procedure: 1) generate one imputed data set, 2) take a bootstrap sample from that 3) estimate beta 4) repeat 1-3 1000 times, 5) summarize the distribution of 1000 beta's. I think something like this might work, but you really ought to do some simulation to confirm it.