Help with lmer formula

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
here.

Cheers,

Josh

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

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Joshua Wiley
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Programmer Analyst II, Statistical Consulting Group
University of California, Los Angeles
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