search for: jasa

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

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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.