Andrew,
I think you mean....
dumb.example2$coefficients = averaged.models
~~~~~~~
currently your code adds a new vector 'coeff' to the gam object, rather
than modifying 'coefficients'. (A good example of why forgiving
languages like R are dangerous, and you should really write everything
in something utterly unforgiving like C - only kidding).
Simon
On 03/12/12 05:01, Andrew Crane-Droesch wrote:
Hi Simon,
Thanks for your help. I've got another question if you don't mind -- is
it possible to "swap out" a set of coefficients of a gamObject in order
to change the results when that gamObject is plotted? The (silly)
example below illustrates that this is possible with the Vp matrix. But
it is not working for me as I'd like it to for the coefficients.
library(mgcv)
#Random data
x = runif(1000,0,1)
y = (log(x^2)+x^3)/sin(x)
dumb.knots = c(.1,.2,.3)
dumb.example1 = gam(y~s(x,k=3),knots=list(x=dumb.knots))
plot(dumb.example1)
x = runif(1000,0,1)
y = (log(x^2)+x^3)/sin(x)
dumb.knots = c(.1,.2,.3)
dumb.example2 = gam(y~s(x,k=3),knots=list(x=dumb.knots))
plot(dumb.example2)
cbind(dumb.example1$coeff,dumb.example2$coeff)
averaged.models=(dumb.example1$coeff+dumb.example2$coeff)/2
correc = matrix(5,3,3)#5 is totally arbitrary, standing in for a proper
MI correction
changed.vcv=correc+(dumb.example1$Vp+dumb.example2$Vp)/2
par(mfrow = c(1,2))
plot(dumb.example2,ylim=c(-500,200))
dumb.example2$coeff = averaged.models
dumb.example2$Vp = changed.vcv
plot(dumb.example2,ylim=c(-500,200))
The confidence bands expand but the location of the fit doesn't change!
What part of the gamObject controls the plot of the smooth?
On 12/02/2012 02:15 AM, Simon Wood wrote:
Hi Andrew,
mgcv matches the knots to the smooth arguments by name. If an element
of 'knots' has
no name it will be ignored. The following will do what you want...
dumb.example = gam(y~s(x,k=3),knots=list(x=dumb.knots))
best,
Simon
On 29/11/12 23:44, Andrew Crane-Droesch wrote:
Dear List,
I'm using GAMs in a multiple imputation project, and I want to be able
to combine the parameter estimates and covariance matrices from each
completed dataset's fitted model in the end. In order to do this, I
need the knots to be uniform for each model with partially-imputed
data. I want to specify these knots based on the quantiles of the
unique values of the non-missing original data, ignoring the NA's. When
I fit the GAM with the imputed data included, I don't want mgcv to use
the data that it is supplied to figure out the knots, because this will
lead to un-comparable results when the many fitted models are combined.
Here is a caricatured example of what I want to do:
#Random data
x = runif(1000,0,1)
y = (log(x^2)+x^3)/sin(x)
example = gam(y~s(x))
plot(example)
#But I want to define my own knots
dumb.knots = c(.7,.8,.9)
dumb.example = gam(y~s(x,k=3),knots=list(dumb.knots))
plot(dumb.example)
dumb.example2 = gam(y~s(x,k=3))
plot(dumb.example2)
Dumb example 1 is the same as dumb example 2, but it shouldn't be.
Once I figure out how to do this, I'll take the fitted coefficients from
each model and average them, then take the vcv's from each model and
average them, and add a correction to account for within and between
imputation variability, then plug them into a gamObject$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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--
Simon Wood, Mathematical Science, University of Bath BA2 7AY UK
+44 (0)1225 386603 http://people.bath.ac.uk/sw283
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