Displaying 4 results from an estimated 4 matches for "gssanova".
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ssanova
2003 Mar 02
0
gss_0.8-2
...us new
functionalities have been added since my last r-announce post.
An ssanova1 suite has been added since version 0.7-4. It implements
low-dimensional approximations of the smoothing spline ANOVA models
of the ssanova suite. ssanova1 scales much better than ssanova with
large sample sizes.
A gssanova1 suite is added for non Gaussian regression. Similar to
ssanova1, it provides better scalability than gssanova. Direct
cross-validation is used in gssanova1 instead of the indirect CV of
gssanova. Currently, only three families are supported: binomial,
poisson, and Gamma; other families of gssan...
2003 Mar 02
0
gss_0.8-2
...us new
functionalities have been added since my last r-announce post.
An ssanova1 suite has been added since version 0.7-4. It implements
low-dimensional approximations of the smoothing spline ANOVA models
of the ssanova suite. ssanova1 scales much better than ssanova with
large sample sizes.
A gssanova1 suite is added for non Gaussian regression. Similar to
ssanova1, it provides better scalability than gssanova. Direct
cross-validation is used in gssanova1 instead of the indirect CV of
gssanova. Currently, only three families are supported: binomial,
poisson, and Gamma; other families of gssan...
2001 Jun 11
2
Generalized Additive Model
Hello,
I am wondering if someone can direct me to the syntax of the Generalised
Additive Model in R? In Splus this is gam(formula, ... inputs would be
pretty much the same as glm I suspect..)
Any response on that (or what package should I grap) would be appreciated
very much.
Thanks,
Peppy Adi-Purnomo
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r-help
2000 Dec 12
1
smoothing binary data
...98.
-Bill
library(MASS)
data(birthwt)
attach(birthwt)
# This doesn't work for me, no matter what h equals
# Maybe I'm doing something wrong
library(sm)
sm.logit(age,low,h=3)
# This "works", but controlling the degree of smoothing
# is problematic
library(gss)
logit.fit <- gssanova(low ~ age,family="binomial")
est <- predict(logit.fit,data.frame(age=age))
plot(lowess(age,1-1/(1+exp(est))),xlab="Age",
ylab="Low Birth Weight",ylim=c(0,1),type='l')
rug(jitter(age[low==0],amount=1))
rug(jitter(age[low==1], amount = 1), side = 3)
-...