I would like to run a mixed effect linear model using lme4. When I attempt to run it I get an error matrix is not avaliable. When I attempt to install Matrix I get a message matrix is not avaliable for my version of R.

I have conducted an experiment with within-subjects Design. Each participant interacted with two systems and after each interaction the same constructs were measured with a questionnaire. The order, in which the participants interacted with the systems, was randomized, so that I have two Groups (system 1, then system 2 and reverse). So my data is nested within each participant, which are nested within a group.

I have conducted an experiment with within-subjects Design. Each participant interacted with two systems and after each interaction the same constructs were measured with a questionnaire. The order, in which the participants interacted with the systems, was randomized, so that I have two Groups (system 1, then system 2 and reverse). So my data is nested within each participant, which are nested within a group.

Is it possible to get the 95 CI for rmse? Thank you

I am running a multilevel growth curve model to examine predictors of social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a continuous numeric variable. The age variable (Index1) has been coded as 0 for age 12, 1 for age 15 and 2 for age 18. I am currently using a time varying predictor, stress (LSI), which was measured at ages 12, 15 and 18, to examine whether trajectory/variation in LSI predicts difference in SA trajectory. LSI is a continuous numeric variable. The data has been converted to long format with SA in 1 column, LSI in the other, ID in another and age in another column. I used the code below to run my model using lmer. However, I get the following error. Please let me know how I can solve this error. Please note that I have 50% missing data in SA at age 12, which I am handling using the REML method.
modelLSI_maineff_RE <- lmer(SA ~ Index1* LSI+ (1 + Index1+LSI |ID), data = LSIDATA, control = lmerControl(optimizer ="bobyqa"), REML=TRUE) summary(modelLSI_maineff_RE)
Error: number of observations (=1080) <= number of random effects (=1479) for term (1 + Index1 + LSI | ID); the random-effects parameters and the residual variance (or scale parameter) are probably unidentifiable