Dear List,
I'm using GAMs in a multiple imputation project, and I want to be able
to combine the parameter estimates and covariance matrices from each
completed dataset's fitted model in the end. In order to do this, I
need the knots to be uniform for each model with partially-imputed
data. I want to specify these knots based on the quantiles of the
unique values of the non-missing original data, ignoring the NA's. When
I fit the GAM with the imputed data included, I don't want mgcv to use
the data that it is supplied to figure out the knots, because this will
lead to un-comparable results when the many fitted models are combined.
Here is a caricatured example of what I want to do:
#Random data
x = runif(1000,0,1)
y = (log(x^2)+x^3)/sin(x)
example = gam(y~s(x))
plot(example)
#But I want to define my own knots
dumb.knots = c(.7,.8,.9)
dumb.example = gam(y~s(x,k=3),knots=list(dumb.knots))
plot(dumb.example)
dumb.example2 = gam(y~s(x,k=3))
plot(dumb.example2)
Dumb example 1 is the same as dumb example 2, but it shouldn't be.
Once I figure out how to do this, I'll take the fitted coefficients from
each model and average them, then take the vcv's from each model and
average them, and add a correction to account for within and between
imputation variability, then plug them into a gamObject$coeffient and
gamObject$Vp matrix, plot/summarize, and have my result. Comments
welcome on whether or not this would be somehow incorrect would be
welcome as well. Still have a lot to learn!
Thanks,
Andrew
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