Good afternoon, I am looking for advice regarding a mixed model I am trying to implement using lme4. My two-level random-effects model won?t run, perhaps due to one or two issues. Level 1 are patients clustered in healthcare facilities (?Station?). The outcome is a continuous variable (?PopCov?) that is calculated at the facility-level, and thus a level 2 variable that does not vary at the patient level. My aim is to examine the prediction of PopCov by (a) patient-level (e.g., race/ethnicity, age, symptom severity), and (b) facility-level variables (e.g., overall racial/ethnic composition, average age). It is important to examine race/ethnicity at both patient and facility-levels because patients with different racial/ethnic backgrounds tend to differ in terms of age, symptom severity, etc. Each record/row in my data set is a patient, with facility-level variables (including PopCov) having identical values among patients within a given facility. An error is thrown when I run a basic model. A1 <-lmer(PopCov ~ (1 | Station), data = DISP) *Error in fn9nM$xeval()) : Downdated VtV is not positive definite I obtain the same error when I add to the model either a patient-level or facility level predictor. An internet search suggested that I have complete separation of my data and/or poorly scaled variables. I assume this issue has to do with the fact that the outcome is a level 2 variable. Perhaps compounding the issue is the large and unbalanced nature of the data. I have ~6 million patients clustered in ~1000 healthcare facilities. Individual facilities have anywhere from 100 to 30000 patients clustered in them. I could use some advice regarding how to specify the model to predict a facility-level variable (level 2) from both patient (level 1) and facility-level (level 2) variables with these data. Thank you in advance. Matt
Downdated VtV error for two level mixed model
2 messages · Matthew Boden, Wolfgang Viechtbauer
Hi Matt, You have already received some answers to your previous post: https://stat.ethz.ch/pipermail/r-sig-mixed-models/2020q3/028806.html https://stat.ethz.ch/pipermail/r-sig-mixed-models/2020q3/028807.html https://stat.ethz.ch/pipermail/r-sig-mixed-models/2020q3/028808.html Best, Wolfgang
-----Original Message----- From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Matthew Boden Sent: Wednesday, 08 July, 2020 19:36 To: r-sig-mixed-models at r-project.org Subject: [R-sig-ME] Downdated VtV error for two level mixed model Good afternoon, I am looking for advice regarding a mixed model I am trying to implement using lme4. My two-level random-effects model won?t run, perhaps due to one or two issues. Level 1 are patients clustered in healthcare facilities (?Station?). The outcome is a continuous variable (?PopCov?) that is calculated at the facility-level, and thus a level 2 variable that does not vary at the patient level. My aim is to examine the prediction of PopCov by (a) patient-level (e.g., race/ethnicity, age, symptom severity), and (b) facility-level variables (e.g., overall racial/ethnic composition, average age). It is important to examine race/ethnicity at both patient and facility-levels because patients with different racial/ethnic backgrounds tend to differ in terms of age, symptom severity, etc. Each record/row in my data set is a patient, with facility-level variables (including PopCov) having identical values among patients within a given facility. An error is thrown when I run a basic model. A1 <-lmer(PopCov ~ (1 | Station), data = DISP) *Error in fn9nM$xeval()) : Downdated VtV is not positive definite I obtain the same error when I add to the model either a patient-level or facility level predictor. An internet search suggested that I have complete separation of my data and/or poorly scaled variables. I assume this issue has to do with the fact that the outcome is a level 2 variable. Perhaps compounding the issue is the large and unbalanced nature of the data. I have ~6 million patients clustered in ~1000 healthcare facilities. Individual facilities have anywhere from 100 to 30000 patients clustered in them. I could use some advice regarding how to specify the model to predict a facility-level variable (level 2) from both patient (level 1) and facility-level (level 2) variables with these data. Thank you in advance. Matt