[R] Help with lmer formula

Joshua Wiley jwiley.psych at gmail.com
Tue Jul 3 06:31:13 CEST 2012

Hi Camila,

In mixed equation form instead of multilevel, it would be:

Y_it = gamma_00 + gamma_10*X_it + gamma_11*W_it*X + (e_it + u_0t + u_1j*X)

your code seems reasonable.  Note that the random intercept and slope
will be correlated in your specification (unstructured if you want, it
is possible to force out, but is sensible starting place)

model <- lmer(Y ~ X + X:W + (X | ID), data = data)

which gives:
residual variance: e_it
variance of intercept (constant term, gamma_00): u_0t
variance of slope (gamma_10): u_1j*X

as well as overall estimates for the intercept, slope of X, and the
interaction of X and W.

Bert is correct that R sig mixed models is the more appropriate list,
but many people read both and there is no reason it cannot be answered



On Mon, Jul 2, 2012 at 6:47 PM, Camila Mendes <cacamendes85 at gmail.com> wrote:
> Hey all -
> I am a newbie on mixed-effects models. I want to estimate the following
> model:
> Y_it = alpha_0t + alpha_1t*X_it + e_it
> alpha_0t = gamma_00 + u_0t
> alpha_1t = gamma_10 + gamma_11*W_it + u_1j
> Where Y is my outcome, X is my level-1 predictor, and W is my level 2
> predictor.
> I am not sure if I am doing it right. Is this the correct specification of
> the formula?
> model = lmer(Y ~ X + X:Y + ( X | ID), data = data)
> Also, can you help me to write down the combined model formula? I tried
> by substituting on the first equation, but I got some weird interactions
> with the residual (?)
> Thanks a lot!
> - Camila
>         [[alternative HTML version deleted]]
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Joshua Wiley
Ph.D. Student, Health Psychology
Programmer Analyst II, Statistical Consulting Group
University of California, Los Angeles

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