Me and my advisor were both working with the same dataset, but I had preemptively filtered mine so there were no table rows where the main variable of interest was NaN. We both ran the same LME (using the fitlme) function, but got slightly different results. Matlab’s write-up of this function, and our understanding of it, is that it censors out the missing data and indeed we saw that we had the same number of observations included in both our models despite my advisor’s table having more rows (that include the NaNs); this is as we expect. But then why were the model fit statistics and fixed effects coefficients slightly different? Most differences were very very small with the exception of one fixed effects coefficient that was drastically different.
The model would be something like this ‘outcome ~ 1 + A + B*C + (1 + A + B*C | subjectID)’ and the NaNs I preemtively filtered were part of the outcome variable where A, B, and C would have actually had data.
(I did re-create both of these results on my own computer over and over again with the only change being my own deletion of the NaN rows).Me and my advisor were both working with the same dataset, but I had preemptively filtered mine so there were no table rows where the main variable of interest was NaN. We both ran the same LME (using the fitlme) function, but got slightly different results. Matlab’s write-up of this function, and our understanding of it, is that it censors out the missing data and indeed we saw that we had the same number of observations included in both our models despite my advisor’s table having more rows (that include the NaNs); this is as we expect. But then why were the model fit statistics and fixed effects coefficients slightly different? Most differences were very very small with the exception of one fixed effects coefficient that was drastically different.
The model would be something like this ‘outcome ~ 1 + A + B*C + (1 + A + B*C | subjectID)’ and the NaNs I preemtively filtered were part of the outcome variable where A, B, and C would have actually had data.
(I did re-create both of these results on my own computer over and over again with the only change being my own deletion of the NaN rows). Me and my advisor were both working with the same dataset, but I had preemptively filtered mine so there were no table rows where the main variable of interest was NaN. We both ran the same LME (using the fitlme) function, but got slightly different results. Matlab’s write-up of this function, and our understanding of it, is that it censors out the missing data and indeed we saw that we had the same number of observations included in both our models despite my advisor’s table having more rows (that include the NaNs); this is as we expect. But then why were the model fit statistics and fixed effects coefficients slightly different? Most differences were very very small with the exception of one fixed effects coefficient that was drastically different.
The model would be something like this ‘outcome ~ 1 + A + B*C + (1 + A + B*C | subjectID)’ and the NaNs I preemtively filtered were part of the outcome variable where A, B, and C would have actually had data.
(I did re-create both of these results on my own computer over and over again with the only change being my own deletion of the NaN rows). fitlme, lme, statistics MATLAB Answers — New Questions

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