Big thx to the team for a wonderful tool !
I would like to analyse data from a randomized controlled trial and I was wondering is there was a module (current or in development) to perform cLDA : constrained Longitudinal Data Analysis in JAMOVI ?
That would be awesome.
Thank-you in advance for your feedback and Merry Christmas !
RJ
constrained Longitudinal Data Analysis
Re: constrained Longitudinal Data Analysis
hi rj,
i don't think there is. but it's not difficult to write one yourself if you've got some R skills:
https://dev.jamovi.org
kind regards and merry christmas
jonathon
i don't think there is. but it's not difficult to write one yourself if you've got some R skills:
https://dev.jamovi.org
kind regards and merry christmas
jonathon
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- Joined: Thu Mar 23, 2017 9:24 pm
Re: constrained Longitudinal Data Analysis
HI
you can use GAMLj Mixed model, which is a module of jamovi.
Assume you have time=0 vs 1, and group (0 vs 1). Your data should be in the long format, so you also have a column with ID for patients. Every patient will have two rows in the dataset, one for time=0 and the other for time=1.
Launch a GAMLj mixed model, set the dependent variable and put time and group as covariates. Go to covariates scaling and set both to "None". Crucially, the fixed effects should be: time and time*group, meaning no main effect of group. You'll notice that in the Fixed Effects panel the software puts all main effect . Remove the main effect of group, this leaves in only the effect of time. On the left panel of Fixed Effects, select both group and time, click on the second arrow dropdown icon, and select "All 2 way". Now you have on the right only the main effect of time and the interaction. That is the cLDA model you're looking for.
Set the random components as "intercept|ID".
Look at the results, the interaction effect `group:time` is the constrained effect of group that you get in a cLDA. All standard errors and F tests are all equal to a cLDA
Here detailed explanations https://datascienceplus.com/taking-the- ... -lda-in-r/
you can use GAMLj Mixed model, which is a module of jamovi.
Assume you have time=0 vs 1, and group (0 vs 1). Your data should be in the long format, so you also have a column with ID for patients. Every patient will have two rows in the dataset, one for time=0 and the other for time=1.
Launch a GAMLj mixed model, set the dependent variable and put time and group as covariates. Go to covariates scaling and set both to "None". Crucially, the fixed effects should be: time and time*group, meaning no main effect of group. You'll notice that in the Fixed Effects panel the software puts all main effect . Remove the main effect of group, this leaves in only the effect of time. On the left panel of Fixed Effects, select both group and time, click on the second arrow dropdown icon, and select "All 2 way". Now you have on the right only the main effect of time and the interaction. That is the cLDA model you're looking for.
Set the random components as "intercept|ID".
Look at the results, the interaction effect `group:time` is the constrained effect of group that you get in a cLDA. All standard errors and F tests are all equal to a cLDA
Here detailed explanations https://datascienceplus.com/taking-the- ... -lda-in-r/
Re: constrained Longitudinal Data Analysis
Excellent, thanks for your reply and support ++
Happy 2021 with Jamovi
Best, RJ1
Happy 2021 with Jamovi
Best, RJ1