Dear Help list,
I am relatively new to the mgcv package, which I am using to model prices of
housing transactions as a function of the characteristics of a home and a
neighborhood. I have several smooth terms to capture price evolution over time,
but also to non-parametrically fit the functional form of some characteristics
such as living area, lot size etc. In my model I have neighborhood fixed
effects (i.e. prices in different neighborhoods can have different means), but
I would also like to allow for within neighborhood correlation in my errors. My
question is: What is the best way to do this?
Sample size is approx. 14,000 obs.
My model (without clustered residuals) looks something like (although I have
many more regressors, several of which are factor variables):
mod.1 <- gam(Price~ s(date.of.sale) + s(livingspace) + s(lotsize) +
factor(neighborhood), data=data, family=Gamma(link=log))
I was thinking that I could either include random effects at the neighborhood
level (s(neighborhood, bs="re")) or I could use a GAMM with correlated errors
within group:
mod.2 <- gamm(Price~ s(date.of.sale) + s(livingspace) + s(lotsize) +
factor(neighborhood), correlation=corSymm(form~1|neighborhood), data=data,
family=Gamma(link=log))
I tried out mod.1 with the random effects and it did provide larger s.e.'s as I
would expect given positive correlation in the residuals. But it also seemed
that the random effects component was not significant if I understand it
correctly: the edf are very close to zero and the significance is NaN. Perhaps
if this is the way to go, I should first demean the data at the neighborhood
level?
As for the gamm approach: I can't get it to work properly: It does not
recognize my groups (i.e. it defines only one group). I tried to correct for
this by transforming the neighborhood numbers into characters
neighborhood.c <- (as.character)
and then used this as the group indicator instead:
corSymm(form~1|neighborhood.c)
But this resulted in an error message: variable lengths differ (found for
'neighborhood.c')Â…The same happens when I write "factor(neighborhood)" in the
corSymm specification. My panel is not balanced, i.e. the number of
observations within neighborhoods varies. Is this a problem? I haven't seen
any indication that the panel must be balanced to use lme, but maybe I've
missed it?
Any feedback would be much appreciated incl. suggestions on where I might read
more about how to use mgcv for this type of problem.
Thanks in advance!
Kathrine
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