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i am struggeling at the following:

My idea is to analyse the development (slope) of an output of different multi level regressions.

The output is matched in my data with 2 different timepoints. I have 3 predictors (senseofhumor, seriousness, friendlyness) These predictors are meassured for many people and groups. And is assume here, that SenseofhumorHIGH (as a special value variable from "senseofhumor" ) might have an impact if its high within a group on the outcome. I also assume the slope might first increase dramatically and than increase slower.

How can I compare different slopes with from different regressions with each other? How is the best way to visualize this slopes?

The code would look something like that:

RandomslopeEC(timepoint1) <- lme(criteria(timepoint1) ~ senseofhumor + seriousness + friendlyness , data = DATA, random = ~ **SenseofhumorHIGH**|group)

RandomslopeEC(timepoint2) <- lme(criteria(timepoint2) ~ senseofhumor + seriousness + friendlyness , data = DATA, random = ~ **SenseofhumorHIGH**|group)

RandomslopeEC(timepoint3) <- lme(criteria(timepoint3) ~ senseofhumor + seriousness + friendlyness , data = DATA, random = ~ **SenseofhumorHIGH**|group)

Thanks a lot in advance

Rajeshwari
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1 Answers1

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it worked out with changing the format from wide to long.

I used:

DATAlong<-  DATA %>%
  gather(`criteriatimepoint1`, `criteriatimepoint2`, `criteriatimepoint3`, key = "timepoint", value = "criteriavalue")

for that.

Afterwards i used

RandomslopeEC <- lme(criteria) ~ senseofhumor*timepoint + seriousness*timepoint + friendlyness*timepoint , data = DATAlong, random = ~ 1|group/timepoint)

for that.

I hope this might others help as well.

Rajeshwari
  • 25
  • 5