# Using lmer in R Studio with a large data set

I have a very large data set (127,000 observations, each point has 22 variables). I am only interested in 7 of those variables. The two dependent variables (linear and rotational acceleration) are continuous, while the rest are categorical.

• Position = 7 factors
• Seniority = 5 factors
• Session = 2 factors
• Season = 6 factors
• Player = 64 different subjects

I would like to include a linear mixed effects analysis of the Linear and Rotational Accelerations across Position, Seniority, and Session (Game or Practice) with Player and Season as random intercepts. My code is below, however I receive the warning message

“ Model failed to converge with max|grad| = 0.0197313 (tol = 0.002, component 1)”

when I try to create either of the models. Is this message appearing because the file is too large? I appreciate that I have over 127,000 impacts in this file, with 22 different variables for each impact – not all of which are needed.

Any guidance in this analysis would be greatly appreciated, as I am still new to using lmer in R.

ThreeYearStudyImpacts$$Location <- factor(ThreeYearStudyImpacts$$Location)
ThreeYearStudyImpacts$$Season <- factor(ThreeYearStudyImpacts$$Season)
ThreeYearStudyImpacts$$ID <- factor(ThreeYearStudyImpacts$$Player.ID)
ThreeYearStudyImpacts$$Position <- factor(ThreeYearStudyImpacts$$Position)
ThreeYearStudyImpacts$$Session <- factor(ThreeYearStudyImpacts$$Session)
ThreeYearStudyImpacts$$Seniority <- factor(ThreeYearStudyImpacts$$Seniority)

modelLin <- lmer(gForce ~ Position + Seniority + Session + (1|ID) + (1|Season), data = ThreeYearStudyImpacts)
modelRot <- lmer(Rotational.Acceleration ~ Position + Seniority + Session + (1|ID) + (1|Season), data = ThreeYearStudyImpacts)