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...his random effect). The model is: X_hijk = alpha_h + h * b_i + r_(ij) + e_hijk , where h = 0 or 1 (indicator) i = 1, ..., N j = 1, ..., n_i k = 1, ..., K alpha is fixed, and the rest are random. I'm willing to assume b, r, and e are mutually independent and normal with var(b) = sigma^2_b, var(r) = sigma^2_r, and var(e) = sigma^2. Any help in writing this model in lme() would be greatly appreciated. Thanks, Chung Cheng

Dispongo de un experimento en el que cinco tratamientos ha sido aplicados a cinco grupos de voluntarios. En cada grupo había tres personas y a cada persona se le tomaron 3 medidas, En total dispongo de 45 medidas, pero evidentemente no son independientes entre sí. Si no tomo en cuenta que las medidas de la misma persona son más parecidas entre sí (bloques anidados) estaría incurriendo en

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu + Bi + Eij). I am using the following program all.fix.coef.lme <- rep(NA,R) all.fix.coef.lm <- rep(NA,R) all.fix.coef.glm <- rep(NA,R) all.se.fix.coef <- rep(NA,R) all.fix.coef.g...