similar to: score in LRT testing for trend

Hi, I'm interested in building a Cox PH model for survival modeling, using 2 covariates (x1 and x2). x1 represents a 'baseline' covariate, whereas x2 represents a 'new' covariate, and my goal is to figure out where x2 adds significant predictive information over x1. Ideally, I could get a p-value for doing this. Originally, I thought of doing some kind of likelihood ratio

configure uses clock_gettime to check whether -lrt is needed - and don''t check other functions. With glibc 2.17 clock_gettime is part of libc, so use timer_gettime instead, which is in -lrt in old and new versions of glibc. Signed-off-by: Olaf Hering <olaf@aepfle.de> --- configure | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configure b/configure index

Dear List, having searched the help and archives, I have the impression that there is no automatic model selection procedure implemented in R that includes/excludes predictors in logistic regression models based on LRT P-values. Is that true, or is someone aware of an appropriate function somewhere in a custom package? Even if automatic model selection and LRT might not be the most appropriate

Hello, is there a R package that provides a log rank trend test for survival data in >=3 treatment groups? Or are there any comparable trend tests for survival data in R? Thanks a lot Markus -- Dipl. Inf. Markus Kreuz Universitaet Leipzig Institut fuer medizinische Informatik, Statistik und Epidemiologie (IMISE) Haertelstr. 16-18 D-04107 Leipzig Tel. +49 341 97 16 276 Fax. +49 341 97 16

Dear expeRts, there is obviously a general trend to use model comparisons, LRT and AIC instead of Wald-test-based significance, at least in the R community. I personally like this approach. And, when using LME's, it seems to be the preferred way (concluded from postings of Brian Ripley and Douglas Bates' article in R-News 5(2005)1), esp. because of problems with the d.f. approximation.

Hello [R]-help I am trying to find > a package where you can do ANOVA based trend analysis on grouped data > using orthogonal polynomial contrasts coefficients, for unequally > spaced factor levels. The closest hit I've had is from this web site: >(http://webcache.googleusercontent.com/search?q=cache:xN4K_KGuYGcJ:www.datavis.ca/sasmac/orpoly.html+Orthogonal+polynomial >l but I

On Wed, May 1, 2013 at 09:21:17AM -0700, Matt Wilson wrote: > On Mon, Apr 29, 2013 at 03:40:13PM +0200, Olaf Hering wrote: > > Ping > > Makes sense. > > Acked-by: Matt Wilson <msw@amazon.com> > > > On Wed, Jan 16, Olaf Hering wrote: > > > > > Ping > > > > > > On Wed, Dec 05, Olaf Hering wrote: > > > > > > >

I have the clinical study data. Year 0 Year 3 Retinol (nmol/L) N Mean +-sd Mean +-sd Vitamin A group 73 1.89+-0.36 2.06+-0.53 Trace group 57 1.83+-0.31 1.78+-0.30 where N is the number of male for the clinical study. I want to test if the mean serum retinol has increased over 3 years among subjects in the vitamin A group. > 1.89+0.36

On my setup with glibc-2.8, benchmark_residual linkage fails with undefines references to clock_gettime(). Adding -lrt fixes that. The following is a small patch for it. Regards. -- O.S. diff --git a/configure.ac b/configure.ac index 993ac33..392485e 100644 --- a/configure.ac +++ b/configure.ac @@ -437,6 +437,11 @@ if test x$enable_stack_smash_protection = "xyes" ; then

I have just committed some code to the r-devel branch to implement the Rao efficient score test. This is asymptotically equivalent to the LRT, but there is some indication that it might have better properties in smaller samples since it is based more directly on the distribution of the sufficient sums under the null hypothesis (e.g., if you have a divergent fit to the model under the alternative,

My CentOS box with: $ uname -r 2.6.18-164.6.1.el5 gets the file order wrong with: ls -lrt * . Two files that were last modified in the same second are shown in order that is the reverse of the order in which they were actually last modified. Note also that the later file was first created after the earlier file was last modified. Is the precision of this only to the second? Mike.

Dear R'ers, When I used S-plus i wrote a small program for a Mantel-Haenszel test for trend (I think it worked). Unfortunately I can't get it working in R. It appears as if my use of 'el' is the problem but I can't sort it out. Error in apply(array, c(, 2, 3), function(el) el * 1:s) : argument is missing, with no default Further down in the program I use 'el'

I'm using pairs() to generate a scatterplot matrix; pairs(~ Fuzzy.gray.white.ratio+Fuzzy.gw.t.score+AgeWhenTested+signal_mean.noise, data=datam,subset=status=="control",main="Controls", labels=c("G/W","Peak Separation","Age","S/N")) How can I add regression lines to the plots?

Hello !! I'm recently having a debate with my PhD supervisor regarding how to write the result of a likelihood ratio test in an article I'm about to submit. I analysed my data using "lme" mixed modelling. To get some p-values for my fixed effect I used model simplification and the typical output R gives looks like this: model2 = update ( model1,~.-factor A) anova (model1,

Dear list users How is it possible to visualise both a linear trend line and a quadratic trend line on a plot of two variables? Here my almost working exsample. data(Duncan) attach(Duncan) plot(prestige ~ income) abline(lm(prestige ~ income), col=2, lwd=2) Now I would like to add yet another trend line, but this time a quadratic one. So I have two trend lines. One linear trend line

(My apologies if this is a repeated posting. I couldn't find any trace of my previous attempt in the archive.) I'm having trouble with forecast() in the dse2 package. It works fine for me on a model without a trend, but gives me NaN output for the forecast values when using a model with a trend. An example: # Set inputs and outputs for the ARMA model fit and test periods

> Subject: [R] LRT in arima models > Date: Mon, 17 Feb 2003 11:53:04 +0100 > From: "vito muggeo" <vito.muggeo at giustizia.it> > To: <r-help at stat.math.ethz.ch> > > Dear all, > > For some reason I'm evaluating the size of the LRT testing for the effect of > some explanatory variable in arima models. > I performed three different simulations

Dear r-help, I would like to use the 'ppm' function of the 'spatstat' package to fit a Strauss inhibition model. I understand that I can specify a parametric model for the "background" trend, but how would I specify a trend which is estimated using a Kernel density smoother? In particular, I would like to use the 'kde' function of the 'ks' package to

R has a Dickey-Fuller Test implementation (adf.test) that tests for unit roots in an autoregressive process with a constant and linear trend. Is there a DF implementation that doesn't use the constant or trend? Thanks, Jake. -- View this message in context: http://www.nabble.com/Dickey-Fuller-Tests-with-no-constant-and-no-trend-tp23565210p23565210.html Sent from the R help mailing list

I have been trying to simulate from a time series with trend but I don't see how to include the trend in the arima.sim() call. The following code illustrates the problem: # Begin demonstration program x <- c(0.168766559, 0.186874000, 0.156710548, 0.151809531, 0.144638812, 0.142106888, 0.140961714, 0.134054659, 0.138722419, 0.134037018, 0.122829846, 0.120188714,