I have two observations from a person each, where every observation corresponds to a different treatment. The treatments are fixed effects, the persons are random effects. I use the command:
model <- lmer(response ~ treatment + (1|Person))
When I run
summary() on this model, I get variances for the residual and the random person effect. I also get the mean values corresponding to what treatment was given. I understand this.
But what's the output from
coef? I don't understand what these things do. I also know nothing about mixed models other than the very basic definition, so I would like a non-technical explanation as opposed to one that says "oh
ranef just gives you the insert technical term here ").
Playing around with things, it seems that the output of 'coef' are sums of the fixef and ranef outputs. The fixed outputs seem to have an obvious explanation, so I guess my question reduces to an inquiry on 'ranef' and 'fitted'. What do these do?
I believe it is especially 'ranef' that I don't undertand. How are these random effects (that's what ranef stands for, no?) when the only estimates we have to work with are parameter estimates for the mean values and a variance estimate for the random effect and a variance estimate for the residual "noise"?