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Note that for this procedure it is crucial that the data are still ordered as we explained at the beginning of Section 2. After making the lagged predictor, we save ��ESM�� as a regular dataframe again, which is needed because the slide() function has the side effect of changing some of the properties of the object. ESM2 stored as ESM$lev1pred has a missing value for the first beep of each day, as well as for those beeps where the person did not provide data on the preceding measurement occasion. learn more Since missing values on predictors are not allowed, when we estimate the AR(1) model the lmer() function will apply listwise deletion to remove all cases with missing predictors from the analysis. It is important to realize that for this reason, the sample Oxymatrine size for the AR(1) model will always be smaller than that for the empty model, with a difference of Np*Nd if there are no missing values, and a larger difference if there are. We can now fit the two-level AR(1) model of Equations (3�C5) by specifying a model equation that has ��lev1pred�� as a predictor with a random effect over persons: twolevel.AR Since we activated the lmerTest package before running our analysis, the output includes p-values (based on the Satterthwaite approximation also used in SAS Proc Mixed, cf. Kuznetsova et al., 2015) for the t-tests of the fixed effects in the model2. The fixed inertia was significant for each outcome, with t(98.78) = 13.2, p Ivacaftor t(95.58) = 16.13, p