How many parameters in my mixed effect model (for t-tests) I have the following mixed effect model;
penicillin_model = lmer(yield ~ treat + (1 | blend),
                    data = penicillin)

Treat contains 4 categories and blend contains 5 categories, am I correct in thinking that there are 9 parameters in my model? (one as the intercept, 3 fixed effects and 5 variances)? or would i need to add an error term? Hope that makes sense.
df = nrow(penicillin) - 9

#calculate p-value by comparing the t-value against the df
1 - pt(0.3643396, df) #treat B
1 - pt(1.8216981, df) #treat C
1 - pt(0.7286793, df) #treat D 

model output


You have four fixed effects - one for each level of group (well, except the reference, but this is replaced by the intercept).
You have two random effects: random intercept (of blend) and residual.
$4 + 2 = 6$ parameters.
