Latent variable regression in lme4 as in HLM
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 at 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 at 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 at r-project.org> on behalf of Simon Harmel <sim.harmel at 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('
')
m1 <- lmer(y ~ year + (1|stid), data = dat) #### 'stid' = student
id
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