Using lme4 how does one define a 2 factor factorial model with both factors
being random?
Specifically I am just trying to recreate the results from Montgomery's
Design of Experiments book (7th edition), example 13.2. In this example
there are 2 random factors and I want to include the interaction
ld appear that f(y) = dlnorm(y,1.66,0.25) and
> F(y) = plnorm(y,1.66,0.25). Note that instead of using 1-F(cens.time) you
> can use plnorm(cens.time,1.66,0.25,lower=TRUE) and that instead of taking
> logs explicitly you can set log=TRUE in the calls to dlnorm() and plnorm().
>
> c
I'm trying to figure out if there is a way in R to get the loglikelihood of
a distribution fit to a set of data where the parameter values are fixed.
For example, I want to simulate data from a given alternate lognormal
distribution and then I will fit it to a lognormal distribution with null
param
I have an unbalanced design I would like to run a power analysis on.
What I have been able to find has pointed me to using the pwr.f2.test
function as described below. My problem is that I don't know how to
appropriately define the numerator and demoninator df.
If someone can help here is some mor
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