jamovi

sounds like you're on the right track ... you'll be wanting to use an "independent samples t-test". one column will contain the measure of interest (your dependent variable) and one column will contain their group (those having used biologic therapies before, and those who haven't). if you...

hi,

jamovi uses the quantile function from R, which looks to default to "type 7" quantiles.


you can reproduce with the following R code:

quantile(c(5, 8, 10, 15, 18, 25, 30, 35, 40, 45))

cheers

jonathon

ah, thanks for letting us know ... just pushing out a 2.6.22 ...

jonathon

thanks yuri,

damo should get a chance to look at this soon.

jonathon

thanks vjalby,

i've created a fix, and will have damo review it before merging:

https://github.com/jamovi/jamovi/pull/1590

with thanks

jonathon

thanks, i've created an issue here:

https://github.com/jamovi/jamovi/issues/1588

jonathon

take a look at jTransform in the jamovi library

jonathon

so in statistics, we treat a missing value as "don't know" ... so if you take the mean of three values, and one of the values is "don't know", then you "don't know" the mean of those three values either. ignore_missing=1 is where you say to ignore the missing value, so ...

i *would* treat the 99 as a missing value, and then use the ignore_missing=1 argument to the MEAN() function.

cheers

Hi, thanks for raising this. so called "multiple response variables" are something of an area of weakness in jamovi at the moment. i've made a couple of changes to jamovi to better support this; i've added an ignore_missing argument to the HLOOKUP() function, and i've added a COUNT() funct...