- #1
FallenApple
- 566
- 61
So I know that linear mixed models model has coefficients that are fixed and random. From what I understand, the fixed coefficients are still good since the random slopes/intercepts capture between subject heterogeneity. Which I suppose help estimate the crossectional effects better( i.e the fixed Betas)
Here is an example of LME
##Y_{i,j} \sim (\beta_{0}+b_{0,i} )+(\beta_{1}+b_{1,i})X_{i,j}+\epsilon_{i,j}##
Where ##(b_{0,i},b_{0,i})\underset{i,i,d}\sim N((0,0),G)##
##\epsilon_{i,j}\underset{i,i,d}\sim N(0,\sigma_{\epsilon})##
So I see here that the "coefficient"s consists of a random part and a fixed part
But I heard that LMEs are just special cases of Generalized Linear Mixed Effects Models(GLMMs). But isn't that a contradiction? GLMMs estimates are only for within subjects. Whereas for LMEs, I have population based estimates and within subject based ones.
Here is an example of GLMM
##link(\mu_{i,j}) \sim \beta_{0} +\beta_{1}X_{i,j}##
Where here appearantly, the betas are random.
So how can the lme be a subcase of glmm if there is no fixed component about which the individual can vary?
Here is an example of LME
##Y_{i,j} \sim (\beta_{0}+b_{0,i} )+(\beta_{1}+b_{1,i})X_{i,j}+\epsilon_{i,j}##
Where ##(b_{0,i},b_{0,i})\underset{i,i,d}\sim N((0,0),G)##
##\epsilon_{i,j}\underset{i,i,d}\sim N(0,\sigma_{\epsilon})##
So I see here that the "coefficient"s consists of a random part and a fixed part
But I heard that LMEs are just special cases of Generalized Linear Mixed Effects Models(GLMMs). But isn't that a contradiction? GLMMs estimates are only for within subjects. Whereas for LMEs, I have population based estimates and within subject based ones.
Here is an example of GLMM
##link(\mu_{i,j}) \sim \beta_{0} +\beta_{1}X_{i,j}##
Where here appearantly, the betas are random.
So how can the lme be a subcase of glmm if there is no fixed component about which the individual can vary?
Last edited: