search for: nonrandomized

Displaying 16 results from an estimated 16 matches for "nonrandomized".

2004 Sep 14
1
R-2.0.0 CMD check . and datasets
Hello everyone I'm having a little difficulty with R-2.0.0 CMD check. My field is Bayesian calibration of computer models. The problem is that I have a large collection of toy datasets, that in R-1.9.1 were specified with lines like this: x.toy <- 1:6 y.toy <- computer.model(x.toy) z.toy <- reality(x.toy) in file ./data/toys.R ; functions computer.model() and reality() are
2004 Sep 14
1
R-2.0.0 CMD check . and datasets
Hello everyone I'm having a little difficulty with R-2.0.0 CMD check. My field is Bayesian calibration of computer models. The problem is that I have a large collection of toy datasets, that in R-1.9.1 were specified with lines like this: x.toy <- 1:6 y.toy <- computer.model(x.toy) z.toy <- reality(x.toy) in file ./data/toys.R ; functions computer.model() and reality() are
2009 Jun 19
0
package JM -- version 0.3-0
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
2010 Mar 18
0
package JM -- version 0.6-0
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
2010 Dec 15
0
package JM -- version 0.8-0
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.
2004 Aug 16
0
Interacting with Clusters...
...g on developing this sort of point-and-click capability in R? I would be very interested in becoming an alpha or beta tester of this functionality. If some computer scientist is looking for a thesis topic involving applications of graphics and statistical methods in health care (randomized or nonrandomized studies), I would be happy to send a recent white paper on the methodology I visualize. On top of my work at Lilly, I am an adjunct professor of biostatistics at Indiana University Medical School and would be willing to participate on a thesis committee. For example, I can perform almost all o...
2009 Jun 19
0
package JM -- version 0.3-0
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
2009 Dec 16
0
Duration model with sample selection (or selectivity)
Hello All, I am interested in estimating a duration model (also known as survival analysis or event-history analysis). I use an economic dataset. In economics terms, the model is "duration model with sample selection (or selectivity)." The time spell variable is only observed for a sample that meets certain requirements so the sample is nonrandom. Does anybody know any R package that
2010 Mar 18
0
package JM -- version 0.6-0
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
2010 Dec 15
0
package JM -- version 0.8-0
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.
2011 Sep 28
0
package JM -- version 0.9-0
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
2011 Sep 28
0
package JM -- version 0.9-0
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
2012 Jul 10
0
package JM -- version 1.0-0
Dear R-users, I'd like to announce the release of version 1.0-0 of package JM (already 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
2012 Jul 10
0
package JM -- version 1.0-0
Dear R-users, I'd like to announce the release of version 1.0-0 of package JM (already 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
2007 Feb 10
1
SAS, SPSS Product Comparison Table
Hi All, My paper "R for SAS and SPSS Users" received a bit more of a reaction than I expected. I posted the link (http://oit.utk.edu/scc/RforSAS&SPSSusers.pdf) about 12 days ago on R-help and the equivalent SAS and SPSS lists. Since then people have downloaded it 5,503 times and I've gotten lots of questions along the lines of, "Surely R can't do for free what [fill in
2008 Jul 08
8
Sum(Random Numbers)=100
Hi R, I need to generate 50 random numbers (preferably poisson), such that their sum is equal to 100. How do I do this? Thank you, Shubha This e-mail may contain confidential and/or privileged i...{{dropped:13}}