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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

enter image description here

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  • $\begingroup$ Look in the output. How many parameters do you see? (Count the fixed effects, count the random effects). $\endgroup$ Commented Jun 25, 2022 at 17:50
  • $\begingroup$ @JeremyMiles Ok, so I have 4 fixed effects (one in the intercept) but the random effects are confusing me a little, would this be 5 as i have 5 types of blend or 2 as in the output i have 2 groups (blend and residual). I have updated the question with this info in the output $\endgroup$
    – Joe
    Commented Jun 25, 2022 at 17:57
  • $\begingroup$ There are no parameters you can't see. $\endgroup$ Commented Jun 25, 2022 at 21:40

1 Answer 1

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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.

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