Applying "iffmiss" filter

Discuss the jamovi platform, possible improvements, etc.

by Janne_J » Fri Aug 12, 2022 7:13 am

Hi dear community,

I am wondering about how to properly apply the "IFFMISS" function to my data. I have a huge set of data and want to exclude all subjects that have at least one missing value in one of my variables of interest (clear cell, or code defined as missing). As I don´t want to delete them by hand (which would take ages) I am sure there must be a more elegant and easier way to filter them out e.g. the "IFMISS" function.

I would create a new nominal variable (0,1) that marks those whose data set is complete or not. Unfortunately, I don´t know what else I have to define to make it work for my purpose.

I hope someone understands the issue and can give me some advise: :slightly_smiling_face:

Thank you in advance!

Regards, Janne
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by Bobafett » Fri Aug 12, 2022 10:11 am

If they have missing data, then wouldn't they already be excluded from any analysis involving that variable? Or are you wishing to exclude them from all future analyses on other variables? If so, can't you set up a filter that would exclude anyone not having a score on this variable?
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by MAgojam » Fri Aug 12, 2022 3:50 pm

Hey @Janne_J,
@Bobafett has already brought to your attention some suggestions, to which I would like to add, with this screenshot, the possibility of using the module Rj+ v2.0.5 to push you further.
Rj_counts_na.PNG
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Mastichi R ? (non indispensabile).
Ho inserito 4 semplici righe di codice, che potrebbero fare al caso tuo.

In the list of variables, you can insert the variables you are interested in considering for the missing data and the code run will generate a new variable in the sheet (counts_na) which will contain the number of missing for each case (row) for the variables present in the list.

If that's not what you were looking for, come back.

Cheers,
Maurizio
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by jonathon » Tue Aug 16, 2022 2:16 am

and another suggestion :)

Screen Shot 2022-08-16 at 12.15.34 pm.png
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