Dear list,
I am interested in fitting a Generalized Additive Mixed Model with
spatially correlated errors to a large, spatially indexed, data set
(~4000 observations).
My initial analysis was a Generalized Additive Model that included a two
dimensional smooth term to model spatially
I'd like to compare tests based on the mixed model representation of
additive models, testing among others
y=f(x1)+f(x2) vs y=f(x1)+f(x2)+f(x1,x2)
(testing for additivity)
In mixed model representation, where X represents the unpenalized part of
the spline functions and Z the wiggly
I'd like to compare tests based on the mixed model representation of additive
models, testing among others
y=f(x1)+f(x2) vs y=f(x1)+f(x2)+f(x1,x2)
(testing for additivity)
In mixed model representation, where X represents the unpenalized part of the
spline functions and Z the wiggly parts,
I wonder whether any of you know of an efficient way to calculate the
approximate degrees of freedom of a gamm() fit.
Calculating the smoother/projection matrix S: y - \hat y and then its trace by
sum(eigen(S))$values is what I've been doing so far- but I was hoping there
might be a more
On Wed, 1 Nov 2006, Fabian Scheipl wrote:
Calculating the smoother/projection matrix S: y - \hat y and then its
trace by sum(eigen(S))$values is what I've been doing so far- but I was
hoping there might be a more efficient way than doing the spectral
decomposition of an NxN-matrix.
Well,
Hello,
I have two gamm question (I am using gamm in mgcv).
1. In have, say 5 time series. Monthly data, 20 year. The 5 time series are
from 5 stations. The data are in vectors, so I have fitted something along the
lines of:
tmp-gamm(Y ~ s(Year,by=station1)+s(Year,by=station2)+
Sorry for the delay replying: I was on holiday, but have foolishly come
back.
I am a bit confused about gamm in mgcv. Consulting Wood (2006) or
Ruppert et al. (2003) hasn't taken away my confusion.
In this code from the gamm help file:
Hello,
I am a bit confused about gamm in mgcv. Consulting Wood (2006) or Ruppert et
al. (2003) hasn't taken away my confusion.
In this code from the gamm help file:
b2-gamm(y~s(x0)+s(x1)+s(x2)+s(x3),family=poisson,random=list(fac=~1))
Am I correct in assuming that we have
Hello,
I have a response variable that is a time series of 0's and 1's. And a couple
of continous explanatory variables.
I would like to fit a gamm with auto-correlation and binomial distribution
using gamm in mgcv. Something simple like:
tmp-gamm(y ~ s(x),
This is a bug. The `prior.weights' element of the the faked `gam' object
(i.e. `test$gam$prior.wieghts' below) has been set to the varIdent()
variance function, rather than the weights that this eventually
represents. I'll fix this for the next patch release, (as soon as I get
any time to do
Hello,
Why would I get an error message with the following code for gamm? I
want to fit the a gam with different variances per stratum.
library(mgcv)
library(nlme)
Y-rnorm(100)
X-rnorm(100,sd=2)
Z-rep(c(T,F),each=50)
test-gamm(Y~s(X),weights=varIdent(form=~1|Z))
summary(test$lme) #ok
Is it possible to set the degrees of freedom for the smooth term in a gamm
to a specfic value?
This can be done using gam in mgcv as follows:
tst.gam-gam(y~s(x, k=6, fx=T))
However, this doesn't seem to work with gamm:
tst.gamm-gamm(y~s(x, k=6, fx=TRUE, bs=cr))
Instead, this
In the summary of the gam object produced by gamm, the Approximate
significance of smooth terms appears to be a test of the improvement in fit
over a linear model, rather than a test of the significance of the overall
effect of x on y:
test.gamm-gamm(y~te(x, bs=cr), random=list(grp=~1))
Dear group,
I am trying to use gamm() in mgcv. Here's the
scenario. The data frame has approx. 110K
observations with information on paediatric
readmission binary outcome (Y/N) and total volume of
their most responsible physician as the covariate.
Since any physician can have multiple
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