similar to: Duration model with sample selection (or selectivity)

Hello All, I am trying to create a legend for a black-white graph. The package I use is ggplot2. It can add colors to the legend key but not line types. Can you please help? # example from Wickman (2009, ggplot2 - elegant graphics for data analysis, page 109) library(ggplot2) huron <- data.frame(year=1875:1972, level=LakeHuron) ggplot(huron, aes(year)) +

Dear All, I have a question about package "ismev", its function "gpd.fit", and interpretation of the results. I used the package ismev to do an extreme value analysis on a fire dataset. Two variables are used in the analysis. The focal variable is acreage burned per fire, ranging from 1 to 5000 acres per fire. In total, there are 69,980 observations. The date covers

Hello all, I have a question about basic statistics. Given a PDF value of 0.328161, how can I find out the value of -0.625 in R? It is like reversing the dnorm function but I do not know how to do it in R. > pdf.xb <- dnorm(-0.625) > pdf.xb [1] 0.328161 > qnorm(pdf.xb) [1] -0.444997 > pnorm(pdf.xb) [1] 0.628605 Many thanks, Edwin -- View this message in context:

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

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

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

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

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

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

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

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

I'm trying to learn how to use R to: * Make a random partition of a data frame between in-sample and out-of-sample * Estimate a model (e.g. lm()) for the in-sample * Make predictions for all observations * Compare the in-sample error sigma against the out-of-sample error sigma. I came up with the following code. I think it's okay, but I can't help feeling this is

>> Hi, I am trying to draw a random sample from an household survey with sample weight. Is there any function in R or Splus which allows this. Regards, ******************************************************* Mehtabul Azam Department of Economics Southern Methodist University Dallas TX 75275-0496 Tel: (214) 214 938 3906 Email: mazam at smu.edu <mailto:mazam at smu.edu>

Found the solution to my 2.0.6 dying on me on SGI - SYSV_IPC needed to be #undef'ed in source/include/includes.h in favor of MMAP, seems to have fixed it. If I run the daemon in server mode, no prob. If I switch to domain mode (and use smbpasswd to join into the domain, per the instructions), I get the error indicating that the username/password pair was incorrect, as if I typed in a wrong

Greetings glmmADMB function users, I am trying to run a series of models using the glmmADMB function with several different distribution families (e.g., poisson, negbinom). I am using a Optiplex 790 PC with Windows 7, 16.0 GB of RAM and a 64-bit operating system. I am running R version 2.15.0 and started out using the most recent version of glmmADMB (I believe version 7.2.15). My data is zero