HI,

I am using lmer() for a simple mixed effects model. The model is of the form
logit(y)~ x + (1|z), where x is an indicator variable and z a multi-level
factor.

I would like an estimate of the response variable (either y or logit y) with
an associated confidence interval for a given value of x.

There does not appear to be a predict function written for lmer().

The output for the fixed effects gives a standard error for the intercept,
the coefficient of x and the correlation. For n observations, I transform
the std errors to variances, and with the correlation, I think I can use the
formula Var(intercept + x) = Var(intercept) + Var(x) + 2Cov(x,intercept) to
get the variance of the fixed effects component of the response variable.

However, I would like to include the random effects component of the
variance, so I may derive a standard error and confidence interval for the
variance.

Does anyone know how to do this? Is there a ready made function like
predict() or does anyone know how to incorporate the variance of random
effects term to derive the std error of the response variable?

Regards,

Brian

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