1)
Due to multiple imputation I cannot use tab, which makes 2 by 2 tables impossible. I was wondering, does this work though?
Code:
forvalues j=0/5{ 2. . tab *_class if implicate==`j', cell nofreq 3. . } | gift_class age_class | <1K <10K <100K 100K+ | Total -----------+--------------------------------------------+---------- 16-25 | 0.32 0.97 2.10 1.94 | 5.32 26-35 | 0.32 1.94 5.81 1.77 | 9.84 36-45 | 0.81 1.94 11.94 7.58 | 22.26 46-55 | 2.26 3.23 17.90 10.32 | 33.71 56-65 | 1.94 1.29 14.84 10.81 | 28.87 -----------+--------------------------------------------+---------- Total | 5.65 9.35 52.58 32.42 | 100.00
2)
I have a single outlier in my key variable that I wanted to look at. I tried to use lvr2plot, but it ididn't recognize the last regression, which was
Code:
mi estimate, esampvaryok: reg job_hours gift_received
Would cook's distnace be usable with MI?
Code:
predict cook, cooksd, if e(sample) . * predict e if e(sample), resid . * list gift_total e cook if cook>4/74
3)
As far as this forum and online tools have told me, most use mi sum as descriptive statistics. What if I have a single variable that has sub variables within itself? For example labour status where the variable goes from 1-9? What could I use? Would it be possible to use this command?
Code:
mi estimate, esampvaryok: total labour_status, over(labour_status)
0 Response to Multiple imputation, descriptives and outliers
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