# LME residual plot returns a huge linear block of residual points

The publicly available data I used

https://content.sph.harvard.edu/fitzmaur/ala2e/cd4.txt

I addressed the unbalanced followup visits by transforming the visit time to the nearest multiple of 8 then de-duplicated visit times.

I then used last observation carried forward to impute missing log CD4 values. Then ran two linear mixed intercept models (log CD4 ~ time) on the raw data and on the imputed data. I checked the residual plots and I got these.

Raw model residuals Imputed model residuals

So I see that my imputation method has made some of the residuals more normal, but why is there that huge group of fitted values around 3? How would I be able to address that?

The models I fit:

raw_3_model <- lme(log_CD4_1 ~ Time, random = ~Time|ID,
data = raw_data, method = "REML",
na.action = na.exclude)

treatment_3_model <- lme(measurements ~ time, random = ~time|ID,
data = final_3, method = "REML",
na.action = na.exclude)

• Could you post the models you fit? – user158565 Dec 7 '18 at 19:30
• @user158565 I just added them in! – j681 Dec 7 '18 at 19:33
• I did not get the results like you did. – user158565 Dec 7 '18 at 23:25
• @user158565 Would there be a way I could see what your residuals look like? Was it of the raw data? – j681 Dec 8 '18 at 22:03
• "transforming the visit time to the nearest multiple of 8 then de-duplicated visit times." In fact, I think I misunderstood this sentence. What exactly did you do on time variable? – user158565 Dec 8 '18 at 22:05