Dear R-Users,
A new book "Learning Regression Analysis
by Simulation (Springer)" is out.
http://www.springer.com/statistics/statistical+theory+and+methods/book/978-4-431-54320-6
http://www.amazon.de/Learning-Regression-Analysis-Simulation-Takezawa/dp/4431543201/ref=sr_1_1?ie=UTF8&qid=1380587
is temporarily available at
the "Papers In Press" place.
Kunio Takezawa(2012): A Revision of AIC for Normal Error Models
Open Journal of Statistics, Vol.2. No.3
http://www.scirp.org/journal/ojs/
**Out now!**
Guidebook to R Graphics Using Microsoft Windows
ISBN: 978-1-118-02639-7
Click he
x, Professor
> Department of Sociology
> McMaster University
> Hamilton, Ontario, Canada
> web: socserv.mcmaster.ca/jfox
>
>
>
> > -Original Message-
> > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
> On
> > Behalf Of Kunio takezawa
> > Sent: October-
derstand why.
2008/10/19, Gabor Grothendieck <[EMAIL PROTECTED]>:
>
> Check out:
>
> http://en.wikipedia.org/wiki/DFFITS
>
>
> On Sun, Oct 19, 2008 at 1:26 AM, Kunio takezawa <[EMAIL PROTECTED]>
> wrote:
> > R-users
> > E-mail: r-help@r-project.org
> >
R-users
E-mail: r-help@r-project.org
Hi! R-users.
I am just wondering what the definition of "dffits" in R language is.
Let me show you an simple example.
function() {
library(MASS)
xx <- c(1,2,3,4,5)
yy <- c(1,3,4,2,4)
data1 <- data.frame(x=xx, y=yy)
lm.out <- lm(y~., data=dat
R-users
E-mail: r-help@r-project.org
Hi!
>I interprete this as following: the simplest tree with xerror under
>min(xerror) + its own xstd
>Neverthless, in some article I read the following rule:
>the simplest tree with xerror under min(xerror) + xstd corresponding to the
>min(xerror)
>Is this a m
R-users
E-mail: r-help@r-project.org
Hi! R-users.
http://finzi.psych.upenn.edu/R/library/mvpart/html/xpred.rpart.html
says:
data(car.test.frame)
fit <- rpart(Mileage ~ Weight, car.test.frame)
xmat <- xpred.rpart(fit)
xerr <- (xmat - car.test.frame$Mileage)^2
apply(xerr, 2, sum) # cross-vali
R-users
E-mail: r-help@r-project.org
Hi! R-users.
A simple object as below was created to see how gam() of
package "mgcv" and anova() work.
function()
{
library(mgcv)
set.seed(12)
nd <- 100
xx1 <- runif(nd, min=1, max=10)
xx1 <- sort(xx1)
yy <- sin(xx1)+rnorm(nd, mean=5, sd=5)
R-users
E-mail: r-help@r-project.org
>but I was specificly interested in calculating QAIC and QAICc from
>a glm fitted with the "family=quasibinomial" option.
If you use "family=quasibinomial(link = "logit")" in glm(),
the program will be:
function ()
{
xx <- c(1,2,3,4,5,6,7,8,9,10)
yy <-
R-users
E-mail: r-help@r-project.org
>My question is: Will this calculation be valid with the residual deviance
>returned by the glm() function using the quasibinomial family as
>reported in R?
Let me show you a simple example, assuming c=2.5:
function ()
{
xx <- c(1,2,3,4,5,6,7,8,9,10)
R-users
E-mail: r-help@r-project.org
My understanding is that package "mgcv" is based on
"Generalized Additive Models: An Introduction with R (by Simon N. Wood)".
On the page 126 of this book, eq(3.4) looks a quartic equation with respect
to
"x", not a cubic equation. I am wondering if all routi
R-users
E-mail: r-help@r-project.org
Murdoch>You may have a binary install of the package; you need the source.
Look
Murdoch>in http://cran.r-project.org/src/contrib/gam_0.98.tar.gz, files
Murdoch>gam/src/backfit.f and gam/src/backlo.f
Thank you very much.
I obtained the source codes of backfi
R-users
E-mail: r-help@r-project.org
My R is R2.61 + fields 4.1 + spam0.31-1.
I tried Tps() in fields like
function ()
{
library(fields)
xx <- matrix(c(1,2,3,2,4,5,10,30,20,40,50,90), ncol=2)
print(xx)
yy <- c(5,1,2,3,4,5)
out1 <- Tps(xx,yy, lambda=1000)
print(out1$fitted.values)
print(out1
R-users
E-mail: r-help@r-project.org
>> I found the answer myself.
>> '.Fortran("baklo",' in lo.wam() and .Fortran("bakfit",in
>> s.wam() may carry out backfitting. But I cannot
>> create an R code which gives the same results as those of
>> "bakfit". If someone knows the detail of "bakfit" alg
R-users
E-mail: r-help@r-project.org
I found the answer myself.
'.Fortran("baklo",' in lo.wam() and .Fortran("bakfit",in
s.wam() may carry out backfitting. But I cannot
create an R code which gives the same results as those of
"bakfit". If someone knows the detail of "bakfit" algorithm,
pleas
R-users
E-mail: r-help@r-project.org
> This iteration seems to be for "iteratively reweighted least squares" not
>for backfitting. And lm.wfit may solve multiple linear equation using
>QR decomposition; but I am not sure.
Let me tell you something about my guess above.
The iteration below is f
R-users
E-mail: r-help@r-project.org
>Please don't ask the same question multiple times!
I am really sorry about it. I thought that my first mail did not
work.
>And no, backfitting and QR are unrelated concepts. You need to read up
>on the theory,
To derive an additive model, we have two
R-users
E-mail: r-help@r-project.org
I have a quenstion on "gam()" in "gam" package.
The help of gam() says:
'gam' uses the _backfitting
algorithm_ to combine different smoothing or fitting methods.
On the other hand, lm.wfit(), which is a routine of gam.fit() contains:
z
R-users
E-mail: r-help@r-project.org
I have a quenstion on "gam()" in "gam" package.
The help of gam() says:
'gam' uses the _backfitting
algorithm_ to combine different smoothing or fitting methods.
On the other hand, lm.wfit(), which is a routine of gam.fit() contains:
z
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