Displaying 20 results from an estimated 27 matches for "jasa".

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casa

2006 Jan 10

8

Simple shaping question

I have linux box (does nat and firewall for small network) connected
to dsl. I want to set priorities for protocols (that nothing could
disturb web browsing). This is my rules (eth0 connected to internet):
/sbin/tc qdisc del dev eth0 root
/sbin/tc qdisc add dev eth0 root handle 1 htb default 30 r2q 100
/sbin/tc class add dev eth0 parent 1: classid 1:2 htb rate 900Kbit

2002 Jul 09

2

package relimp

Hi,
i'm newbie for this, but it's very interesting, but how i have to
interpret the results
if i get i.e. this results ?
Is it correct -
if the "Ratio of effect sd" is positiv than the Numerator effects are
bigger , and the negative case vice-versa ?
Ratio of effect standard deviations: 0.954
Log(sd ratio): -0.047 (se 0.828)
Approximate 95% confidence

2008 Jun 19

2

[Bug 16433] New: Text don't go away on redraw

...Product: swfdec
Version: unspecified
Platform: x86 (IA32)
OS/Version: Linux (All)
Status: NEW
Severity: normal
Priority: medium
Component: library
AssignedTo: swfdec at lists.freedesktop.org
ReportedBy: jasa.david at gmail.com
QAContact: swfdec at lists.freedesktop.org
There's changing text in file I'll attach. Problem is, that when "new" line
appears, the old one doesn't go away and characters "stack" on each other.
--
Configure bugmail: http://bug...

2003 Nov 26

1

lines(lowess()) trouble

...e x-min and x-max of a data set?) Rather than speculate
what causes it, I am including a short R snippet and a short data set that
demonstrates it. is this a bug or a feature?
* it would be nice if lowess was a little better documented. I have easy
access to Becker-Chambers-Wilks, but not to JASA and AS. I wish "?lowess"
would tell me a little more about the method.
thanks in advance.
regards, /iaw
-------- test.R
test <- read.table(file="test.txt", sep="\t", header=T);
plot( test$x, test$y, log="xy");
lines( lowess( test$x, test$y, f=0.5...

2009 Feb 15

1

GLMM, ML, PQL, lmer

Dear R community,
I have two questions regarding fitting GLMM using maximum likelihood method.
The first one arises from trying repeat an analysis in the Breslow and
Clayton 1993 JASA paper. Model 3 of the epileptic dataset has two random
effects, one subject specific, and one observation specific. Thus if we
count random effects, there are more parameters than observations. When I
try to run the following code, I get an error saying: "Error in
mer_finalize(ans) : q = 295 &...

2010 Dec 20

0

survexp - unable to reproduce example

...Loading required package: splines
> ## Example from help page of survdiff
> ## Expected survival for heart transplant patients based on
> ## US mortality tables
> expect <- survexp(futime ~ ratetable(age=(accept.dt - birth.dt),
+ sex=1,year=accept.dt,race="white"), jasa, cohort=FALSE,
+ ratetable=survexp.usr)
Error in floor(temp) : Non-numeric argument to mathematical function
> sessionInfo('survival')
R version 2.12.1 Patched (2010-12-18 r53869)
Platform: i386-pc-mingw32/i386 (32-bit)
locale:
[1] LC_COLLATE=German_Switzerland.1252 LC_CTYPE=...

2012 Mar 07

0

sparsenet: a new package for sparse model selection

We have put a new package sparsenet on CRAN.
Sparsenet fits regularization paths for sparse model selection via coordinate descent,
using a penalized least-squares framework and a non-convex penalty.
The package is based on our JASA paper
Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet : Coordinate Descent with Non-Convex Penalties. (JASA 2011)
http://www.stanford.edu/~hastie/Papers/Sparsenet/jasa_MFH_final.pdf
We use Zhang's MC+ penalty to impose sparsity in model selection. This pen...

2012 Mar 07

0

sparsenet: a new package for sparse model selection

We have put a new package sparsenet on CRAN.
Sparsenet fits regularization paths for sparse model selection via coordinate descent,
using a penalized least-squares framework and a non-convex penalty.
The package is based on our JASA paper
Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet : Coordinate Descent with Non-Convex Penalties. (JASA 2011)
http://www.stanford.edu/~hastie/Papers/Sparsenet/jasa_MFH_final.pdf
We use Zhang's MC+ penalty to impose sparsity in model selection. This pen...

2003 Dec 11

1

plot of survival probability vs. covariate

...x)
Now I wanted to make a plot of survival probability
vs. the covariate, and the 95% confidence interval for
the survival probability. It's just like a
Kaplan-Meier Survival curve, except now the x axis
represents the value of covariate, not the time.
Someone gave me a reference to a paper in JASA by Gary
(1992) for this type of plot, but I didn't have the
access to the paper. So I am wondering if anyone knows
how to do this in R or S-Plus?
In addition, can anyone explain to me what are the
following "type" options in predict.coxph()
predicting?
predict(fit,type='lp',...

1998 Jun 30

0

R-beta: stable distribution and stable glm package

...d Lindsey, J.K. (1998) 'Generalized regression models
for heavy-tailed processes based on non-symmetric stable
distributions: a likelihood approach. (To appear in Applied Statistics)'
Note that 'rstable' is not a miracle of computation.
Implementing the Chambers, Mallows and Stuck (JASA 71: 340-44)
algorithm would certainly be preferable for 'rstable'.
Bug reports and suggestions are of course welcome.
Philippe Lambert
--
--------------------------------------------------------
Philippe Lambert Tel: (+32)-4-3663091
Universite de Liege...

2002 Jun 02

0

quantreg 3.09

...eral F-like tests
for quantile regression models at a given quantile, and other F-like tests
for tests of linear restrictions on coefficients of rq() fits for several
quantiles, e.g. tests of equality of slopes across quantile fits.
3. some new plotting routines of the sort appearing in my recent JASA
paper with Olga Geling on survival models for the Carey, et al medfly
experiment data.
Comments would be welcome...
url: http://www.econ.uiuc.edu Roger Koenker
email roger at ysidro.econ.uiuc.edu Department of Economics
vox: 217-333-4558 University of Illinois
fax: 217-244-6678...

2007 Jan 10

0

Implementation of Wei-Lachin Test

Greetings -
Does anybody know if there is an R implementation of the Wei-Lachin
tests for incomplete multivariate observations (JASA 79: 653-661)?
The authors' example of data where this test can be applied is in a
study comparing treatment to placebo where the outcome is a series of
time-to-event observations - i.e., time to development of a nonfatal
symptom in each of several major body systems (dermal, musculoskeletal...

2009 Jul 17

1

package to do inverse probability weighting in longitudinal data

Hi there,
I have a dataset from a longitudinal study with a lot of drop-out. I
want to implement the inverse probability weighting method by Robins
1995 JASA paper "Analysis of semiparametric regression models for
repeated outcomes in the presence of missing data". Does anyone know
if there is a package to do it in R (or other software)? Thanks a lot!
Lei

2007 May 25

0

Competing Risks Analysis

...for "time to event type 2",
with etype=2
.
.
.
Then
fit <- coxph(Surv(time,status) ~ .... + strata(etype), ....
1. Wei, Lin, and Weissfeld apply this to data sets where the competing
risks are not necessarily exclusive, i.e., time to progression and time
to death for cancer patients. JASA 1989, 1065-1073. If a given subject
can have more than one "event", then you need to use the sandwich estimate
of variance, obtained by adding ".. + cluster(id).." to the model
statement above, where "id" is variable unique to each subject.
(The method of fitting found...

2007 Sep 24

1

weighting question

Hi R-users,
Can anyone tell me where can i find info about they way how post stratification weights are calculated when i have an already stratified survey design, especially in Survey Package (but any theoretical material would do me just fine) ?
Thank you and have a nice day!
---------------------------------
[[alternative HTML version deleted]]

2009 Mar 19

0

Testing loess fit versus linear fit.

I would like to experiment with testing the fit of a loess model against
the fit from an ordinary linear regression. The 1988 JASA paper by
Cleveland and Devlin *appears* to indicate that this can be done, at
least ``approximately''. They, as I read it, advocate the use of an
ANOVA type test with degree of freedom chosen to make the ``F ratio''
have an approximate F distribution under the null hypothesis.
(I h...

2011 Mar 10

1

power for repeated-measures ANOVA lacking sphericity

...ANOVA design, e.g., 2 groups, 4
within-subject factors, an average 0.40 correlation between
the 4 within factors, etc.
I don't think I can use power.anova.test() because it does
not consider corr=0.40.
I am hoping that someone has already implemented the method
by Muller & Barton (1989, JASA 84: 549-555) on how to
approximate statistical power for a univariate test in
repeated-measures ANOVAs when the sphericity assumption is
not met. If not, perhaps the MANOVA approach by O'Brien &
Kaiser (1985, Psych Bull: 97, 316-33).
library(pwr) does not seem to support either.
Many...

2012 Nov 26

0

ncvreg question

To whom It May Concern,
I am working on a dimensional reduction problem using Smoothly Clipped
Asolute Deviation (SCAD) Penalty according to "Variable Selection via
Nonconcave Penalized Likelihood and its Oracle Properties, J.Fan and R. Li,
JASA, Dec 2001". I found an R package named *"ncvreg*" which is capable
to perform this variable selection procedure. I noctice, in
function*ncvreg(x, y),
* an intercept is automatically added to the model fitting step. I wonder,
is there anyway to remove the intercept, something similar...

2006 Jun 02

1

IMQ + NAT

Hello,
I have
eth0 - internet
eth1..4 - local networks
on eth0 i do $IPTABLES -A POSTROUTING -t nat -o eth0 -j MASQUERADE
I want to balance out/in load for eth1..4 and localhost (mainly
squid). Nat makes impossible to do it on eth0, so I installed IMQ. I
need to get to on imq0 unnnated in/out traffic that I could make
priorities for protocols and networks. Do somthing like this:

2010 Dec 09

1

survival: ridge log-likelihood workaround

Dear all,
I need to calculate likelihood ratio test for ridge regression. In February I have reported a bug where coxph returns unpenalized log-likelihood for final beta estimates for ridge coxph regression. In high-dimensional settings ridge regression models usually fail for lower values of lambda. As the result of it, in such settings the ridge regressions have higher values of lambda (e.g.