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

[Ben Bolker]

   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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