[R-sig-ME] Latent variable regression in lme4 as in HLM

[Simon Harmel]

Dear James,

Thanks for your response. I should research this a bit more. I have the
feeling that HLM software under the *Latent Variable Regression *tab might
be doing something different. But I highly appreciate you insightful
response.

Dear Ben,  lmerControl(check.nobs.vs.nRE="ignore") didn't work.

library(lme4)
dat <- read.csv('https://raw.githubusercontent.com/hkil/m/master/z.csv')
m1 <- lmer(y~ year + (year|stid), data = dat,
           control=lmerControl(check.nobs.vs.nRE="ignore"))

On Tue, May 26, 2020 at 7:38 PM Ben Bolker <bbolker using gmail.com> wrote:

>    For what it's worth, *if* you're sufficiently sure that your model
> is identifiable, you can override the checks that test the relative
> numbers of observations/levels/etc.; see the "check.*" options in
> ?lme4::lmerControl, and set the relevant ones to "ignore"
>
> On Tue, May 26, 2020 at 7:12 PM Uanhoro, James
> <uanhoro.1 using buckeyemail.osu.edu> wrote:
> >
> > Hello Simon,
> >
> > I'm not sure what HLM does. However: if your question is about using
> > the random intercepts (individuals' starting points) to predict the
> > random slopes (their linear growth rate), then the model you need is:
> >
> > summary(m2 <- lmer(y ~ year + (1 + year | stid), dat))
> >
> > whcih returns the random intercept and a random slope on time.
> >
> > The correlation between both random effects is the regression
> > coefficient from regressing the slope on the intercept (or vice-versa)
> > when both variables are standardized.
> >
> > More generally, you can always obtain regression coefficients from a
> > correlation/covariance matrix of random effects. With a two-by-two
> > correlation matrix, the single correlation is the coefficient (in both
> > directions). In a larger matrix of random effects, you can use the
> > solve() function in R to obtain coefficients from the matrix. See here:
> > https://stackoverflow.com/questions/40762865/how-do-i-get-regression-
> > coefficients-from-a-variance-covariance-matrix-in-r
> >
> > I tried your exact example, and m2 above will not fit because some of
> > your participants have under 2 time points while the maximum number of
> > time points is 3, resulting in a situation where the software is
> > attempting to compute more random effect values than there are rows in
> > the data - the software complains. Also, it is a good idea to rescale
> > that y variable prior to data analysis. I was able to get the model to
> > run by limiting the data to cases with more than 1 recorded time point:
> >
> > summary(m3 <- lmer(y.s ~ year + (1 + year | stid), data = t.dat, subset
> > = n > 1))
> >
> > I arrived at a correlation/coefficient of -0.06.
> >
> > Hope this helps, -James.
> >
> > Sent from Outlook Mobile
> >
> > From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> on
> > behalf of Simon Harmel <sim.harmel using gmail.com>
> > Sent: Tuesday, May 26, 2020, 18:27
> > To: r-sig-mixed-models
> > Subject: [R-sig-ME] Latent variable regression in lme4 as in HLM
> >
> > Dear All,
> >
> > I know that in the HLM software, it is possible to use "intercept"
> > (e.g.,
> > initial place of students at year "0") as the *predictor *of "slope"
> > (e.g.,
> > fixed rate of change in years) under the *Latent Variable Regression
> > *tab.
> >
> > I was wondering if this is also possible in "lme4" or any other
> > mixed-modeling packages in R?  *Thanks, Simon*
> >
> > *## Here is an example dataset for demonstration:*
> > library(lme4)
> > dat <- read.csv('
> >
> https://urldefense.com/v3/__https://raw.githubusercontent.com/hkil/m/master/z.csv__;!!KGKeukY!gjIgidLro6PaJJUHZOY1gk9IW8FfrzGWzo9IEaCRgFwkorvpE1tkLXqn3ujcsmy6OvxsB2xlUIM$
> >  ')
> > m1 <- lmer(y ~ year + (1|stid), data = dat)      #### 'stid' = student
> > id
> >
> >         [[alternative HTML version deleted]]
> >
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