similar to: generate random number

Odette Gaston <odette.gaston <at> gmail.com> writes: > > Dear all, > > I have a problem with accessing class attributes. > I was unable to solve this > yet, but someone may know how to solve it. My best guess at your immediate problem (doing things by hand) is that you're not using the whole vector. From your example: Delta <- c(m1 = 0, m2 = 1.8, m3 =

Hi Uwe and all, Error message was: error in normalizePath(path) : path[1]: no such file to load Many thanks, Odette On Fri, Jan 23, 2009 at 1:22 AM, Uwe Ligges <ligges@statistik.tu-dortmund.de > wrote: > > > Odette Gaston wrote: > >> Hi folks, >> >> I am currently having the problem with using R 2.8.1 that I cannot install >> some of packages from

Odette Gaston wrote: > Hi folks, > > I am currently having the problem with using R 2.8.1 that I cannot install > some of packages from CRAN or local drive and somebody may be able to help > me. > ex) faraway package and lme4 package. I have downloaded them in my hard > drive as local, but still R was unable to find the package (message showed > up as no such file). I

Hi all, does anyone know how to automatically update starting values in R? I' m fitting multiple nonlinear models and would like to know how I can update starting values without having to type them in. thank all --- On Fri, 12/26/08, r-help-request@r-project.org <r-help-request@r-project.org> wrote: From: r-help-request@r-project.org <r-help-request@r-project.org> Subject:

Odette > Wondering how can I generate "Akaike weight" with R? I know the description, > but is there any function to generate by R on the web-site or R library? > I am using GLM or GLMM (family=binomial), so would be appreciated if you > help me. You could have a look at this. http://bm2.genes.nig.ac.jp/RGM2/R_current/library/aod/man/summary.aic.html Which is in the OAD

Hi folks, I am currently having the problem with using R 2.8.1 that I cannot install some of packages from CRAN or local drive and somebody may be able to help me. ex) faraway package and lme4 package. I have downloaded them in my hard drive as local, but still R was unable to find the package (message showed up as no such file). I could download most packages, but not all what I want. I showed

Dear all, Is it possible to generate AIC or something equivalent for nonlinear model estimated based on maximum log likelihood l in R? I used nls based on least squares to estimate, and therefore I cannot assess the quality of models with AIC. nlme seems good for only mixed models and mine is not mixed models. res <- nls(y ~ d*(x)^3+a*(x)^2+b*x+c, start=list(a=2, b=1,c=1,d=1), data=d) If

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Greetings, everyone. I've been trying to analyze bird nest survival data using generalized linear mixed models (because we documented several consecutive nesting attempts by the same individuals; i.e. repeated measures data) and have been unable to persuade the various GLMM models to work with my user-defined link function. Actually, glmmPQL seems to work, but as I want to evaluate a suite of

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

I think the optim() example in the Details section of relist()'s help page is not totally correct. In particular, in the current form it is not taken into account that vcov should be a symmetric matrix and only the parameters in the lower (or upper) triangular part should be optimized. A possible fix is: ipar <- list(mean = c(0, 1), vcov = c(1, 1, 0)) initial.param <-

Dear All, I'd like to ask fro any pointers to code in any package out there that can (even partially) handle the following situation: say we have the linear model # toy data y <- rnorm(100) time <- runif(100, 0, 5) treat <- gl(2, 50, labels = c("placebo", "active")) sex <- gl(2, 1, 100, labels = c("male", "female")) bmi <- runif(100,

Dear R-users, I'd like to announce the release of the new version of package 'ltm' (i.e., ltm_0.9-0 soon available from CRAN) for Item Response Theory analyses. This package provides a flexible framework for analyzing dichotomous and polytomous data under various IRT models. Furthermore, supporting functions for descriptive statistics, goodness-of-fit, ability estimation and

Dear R-users, I'd like to announce the release of the new version of package 'ltm' (i.e., ltm_0.9-0 soon available from CRAN) for Item Response Theory analyses. This package provides a flexible framework for analyzing dichotomous and polytomous data under various IRT models. Furthermore, supporting functions for descriptive statistics, goodness-of-fit, ability estimation and

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modeling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the survival outcome and we wish to account for the effect of a time-dependent covariate measured with error.

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modeling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the survival outcome and we wish to account for the effect of a time-dependent covariate measured with error.

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modeling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modeling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent