Timeline for Linear mixed model for analysis between 2 methods and intra inter observer reliability/agreement
Current License: CC BY-SA 4.0
12 events
| when toggle format | what | by | license | comment | |
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| Oct 6, 2020 at 11:45 | comment | added | K S | sorry that was the wrong model : this one , Gls afi ~ 1 +( 1 + Observer. + scanner. | Patient. ) | |
| Oct 6, 2020 at 11:40 | comment | added | Robert Long | So what did you mean by "the best model i come up with is all random factors" | |
| Oct 6, 2020 at 11:38 | comment | added | K S | this is what the r call looks like for one method: gls 2ds ~ 1 + scanner.+( 1 + Observer. | Patient. ). i think the patient is the intercept, | |
| Oct 6, 2020 at 11:33 | comment | added | Robert Long | And you are fitting random intercepts for scanner and observer ? | |
| Oct 6, 2020 at 11:33 | comment | added | K S | 2 scanners, 2 different observors, and there is about 40 patients. | |
| Oct 6, 2020 at 11:23 | comment | added | Robert Long | OK but how many levels of observer and scanner do you have ? | |
| Oct 6, 2020 at 11:21 | comment | added | K S | I am using jamovi btw which is lme4 based. So i mean the largest r2 and the lowest AIC/BIC to indicate model fit, but im not sure if this is appropriate either. so i have the patient as a random factor/ grouping variable, plus the observer and scanner also as random effects. this is based on 2 separate models for each method. so my longitudinal data headers are : Patient,Scanner, Obs, GLS AFI,GLS 2DS. | |
| Oct 6, 2020 at 10:42 | comment | added | Robert Long |
No problem. It wasn't an answer, just a comment, as there isn't enough information about your data yet. When fitting random intercepts you need to ensure there are enough levels of each factor (6 as a minimum, is a good rule of thumb). Perhaps you can include more info about your study and data (e.g. by including the output of str(data)).What do you mean "all random factors" and what do you mean by "best model" ?
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| Oct 6, 2020 at 10:36 | comment | added | K S | thanks for the answer btw! Yeah that is what i was thinking as well, but i started trialling a few things and the best model i come up with is all random factors , and just a fixed intercept. is this even possible? | |
| Oct 6, 2020 at 8:57 | comment | added | Robert Long | It sounds like you might want crossed random effects, for patient and observer. Does this this help ? | |
| Oct 6, 2020 at 7:42 | review | First posts | |||
| Oct 6, 2020 at 8:19 | |||||
| Oct 6, 2020 at 7:42 | history | asked | K S | CC BY-SA 4.0 |