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...l(x.toy) z.toy <- reality(x.toy) in file ./data/toys.R ; functions computer.model() and reality() are defined in ./R/calibrator.R. [In this application, the (toy) functions computer.model() and reality() are the objects of inference, as per the standard Bayesian approach. The functions are nonrandom in that they are deterministic but random in the Bayesian sense. Thus y.toy and z.toy are observations of (random) functions]. In the Real World, one would have access to x.toy, y.toy, and z.toy but not (of course) computer.model() or reality(). These functions should never be seen or referre...

...l(x.toy) z.toy <- reality(x.toy) in file ./data/toys.R ; functions computer.model() and reality() are defined in ./R/calibrator.R. [In this application, the (toy) functions computer.model() and reality() are the objects of inference, as per the standard Bayesian approach. The functions are nonrandom in that they are deterministic but random in the Bayesian sense. Thus y.toy and z.toy are observations of (random) functions]. In the Real World, one would have access to x.toy, y.toy, and z.toy but not (of course) computer.model() or reality(). These functions should never be seen or referre...

...ng 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 error. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * a relative risk model with a piecewise-constant baseline risk function is now available for the event outcome, using option 'piecewise-PH-GH' in the 'method' argument of jointModel(). * several types of residuals are supported for the long...

...ng 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 error. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * function survfitJM() has been added that calculates predictions of subject-specific survival probabilities given a history of longitudinal responses. * function dynC() has been added that calculates a dynamic concordance index for joint models. The function...

...a 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. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * for all joint models fitted by JM there is now the option to use a pseudo adaptive Gauss-Hermite rule. This is much faster than the default option and produces results of equal or better quality. It can be invoked via the 'method' argument of jointModel...

...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 a...

...ng 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 error. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * a relative risk model with a piecewise-constant baseline risk function is now available for the event outcome, using option 'piecewise-PH-GH' in the 'method' argument of jointModel(). * several types of residuals are supported for the long...

...on 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 can take care of this? Thank you. Edwin

...ng 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 error. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * function survfitJM() has been added that calculates predictions of subject-specific survival probabilities given a history of longitudinal responses. * function dynC() has been added that calculates a dynamic concordance index for joint models. The function...

...a 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. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * for all joint models fitted by JM there is now the option to use a pseudo adaptive Gauss-Hermite rule. This is much faster than the default option and produces results of equal or better quality. It can be invoked via the 'method' argument of jointModel...

...els. 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 covariate measured with error. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * jointModel() with option "spline-PH-aGH" for the 'method' argument can now also handle competing risks settings. * jointModel() with option "spline-PH-aGH" for the 'method' argument can now also handle exogenous time-dependen...

...els. 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 covariate measured with error. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * jointModel() with option "spline-PH-aGH" for the 'method' argument can now also handle competing risks settings. * jointModel() with option "spline-PH-aGH" for the 'method' argument can now also handle exogenous time-dependen...

...els. 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 covariate measured with error. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. Some basic features of JM: * it fits joint models for continuous longitudinal responses and allows for several options for the survival submodel, including PH models with Weibull, piecewise-constant, spline-approximated and unspecified baseline hazard functions. The most complete opti...

...els. 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 covariate measured with error. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. Some basic features of JM: * it fits joint models for continuous longitudinal responses and allows for several options for the survival submodel, including PH models with Weibull, piecewise-constant, spline-approximated and unspecified baseline hazard functions. The most complete opti...

...g Values Analysis(tm) aregImpute (Hmisc), fit.mult.impute (Design) Mixed Models Proc Mixed SPSS Advanced Models lmer Operations Research SAS/OR None TSP Power Analysis SAS/STAT: Power,GLM Power SamplePower(tm) asypow, powerpkg, pwr Regression Models SAS/BASE SPSS Regression Models(tm) R Sampling, Nonrandom SAS/STAT: surveymeans, etc. SPSS Complex Samples(tm) survey Structural Equations SAS/STAT: Calis Amos(tm) sem Text Analysis Text Miner SPSS Text Analysis for Surveys(tm) tm Time Series SAS/ETS(tm) SPSS Trends(tm) ArDec, brainwaver, dyn, fame, Systemfit, tsDyn, tseries, tseriesChaos, tsfa, urca, uro...

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}}