Hello R users,
I may be thinking about this all wrong. If I am, please let me know.
My question is about bootstrapping gam models. I want to know if there is a
way to produce bootstrapped smoothers in a gam model that has more than one
significant explanatory term. I know how to achieve this when the model
only has one term. The code that I am using to do so is below. It produces
a nice graph of y values in response to my explanatory variable (x), along
with bootstrapped smoothers in a grey color and the smoother produced by
the gam package in black. In my mind this is a very useful graph to help
determine the "realness" of the fitted smoother that gam produces. I should
note a thanks to Charles Geyer who had code on his STATS 5601 website that
has helped me immensely thus far.
x <- SI1 ## my explanatory variable
y <- NO3.sp3 ## my dependent/response variable
model <- gam(y ~ s(x, bs = "cr")) ## I know I can use other smoother types.
I am still experimenting with this aspect.
plot(x, y)
curve(predict(model, newdata = data.frame(x = x)), add = TRUE)
n <- length(x)
nboot <- 100
for (i in 1:nboot) {
k.star <- sample(n, replace = TRUE)
x.star <- x[k.star]
y.star <- y[k.star]
model.star <- gam(y.star ~ s(x.star, bs = "cr"))
curve(predict(model.star, data.frame(x.star = x)),
add = TRUE, col = "grey")
}
points(x, y, pch = 16)
curve(predict(model, newdata = data.frame(x = x)), add = TRUE, lwd = 2)
But now lets say I have a model with two explanatory variables ( x and z)
and for the sake of this discussion, both terms are significant (i.e., the
data is better explained by having them in the model). The model in this
case would be:
new.model <- gam(y ~ s(x, bs = "cr")+ s(z, bs = "cr"))
I know by using "plot (new.model, resid = T, pch = 16)" I can see the
smoothers and 95% CI produced by the gam package with the data values
overlayed.
Is there a way to produce bootstrapped smoothers for each term from the
same model (new.model) so that I can visualize them in the same way as the
plot function allows me? I thought of having separate single-term models
for each explanatory variable (Y~s(x) and y~s(z)), but doing so is not
correct since removing one term from a model causes the parameter values of
the other term to change. Any suggestions on the bootstrapping code that
would produce such a graphical output would be appreciated, especially
since I am new to the bootstrapping coding.
I hope that is clear and thanks in advance for any help that anyone can
offer.
--
Basil Iannone
University of Illinois at Chicago
Department of Biological Sciences (MC 066)
845 W. Taylor St.
Chicago, IL 60607-7060
Email: bian...@uic.edu
Phone: 312-355-3231
Fax: 312-413-2435
http://www2.uic.edu/~bianno2
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