similar to: sequential treatment of a vector for formula

Dear all, I am having a (really) hard time getting pyears to work together with a ratetable to give me the number of expected events (deaths). I have the following data: dos, date of surgery, as.Date dof, date of last follow-up, as.Date dos, date of surgery, as.Date sex, gender, as.factor (female,male) ev, event(death), 0= censored at time point dof, 1=death at time point dof Could someone

Hello, I have a question about the function lifetab in package KMsurv. The description of the output value surv says "the estimated survival function at the start of the intervals". Are these estimates the ones calculated via Kaplan-Meier probability of survival ? Thanks in advance! -- View this message in context:

Dear R-users, I would like to show you a simple example that gives an overview of one of my current issue. Although my working setting implies a different parametric model (which cannot be framed in the glm), I guess that what I'll get from the following example it would help for the next steps. Anyway here it is. Firstly I simulated from a series of exposures, a series of deaths (given a

I am interning in a computer science lab and I'm very new to R. The language basics are clear, but this particular problem isn't: I have a very large dataframe called "data" which holds data from Halo matches. I'm trying to analyze a certain window such that data$deaths>20 and data$deaths<=27. When I enter the line kills = data$kills[data$deaths>20] or any single

Hello, For insect mortality data I'm trying to get an R script that will take the data from the raw form and convert it to Lx (% survival) for a number of treatments. The raw data has the number of days lived for each individual for the respective treatment. Thus, for example, when R selects the data for a single treatment I end up with the following vectors: >day=seq(from=0,to=6)

Reading "Modern Applied Statististics with S" and trying the corresponding examples both in the book and in ../lib/R/library/MASS/script, I'm now trying chapter 4 plotting bars with the following code on a linux box with R 1.8.1: ---------------------- library(MASS) library(lattice) options(echo=T, width=65, digits=5) lung.deaths <- aggregate(ts.union(mdeaths, fdeaths), 1)

The following is evidence of what is surely an undesirable feature. The issue is the handling, in calls to model.frame(), of an explanatory variable that has been derived as an unclassed factor. (Ross Darnell drew this to my attention.) ## Data are slightly modified from p.191 of MASS > worms <- data.frame(sex=gl(2,6), Dose=factor(rep(2^(0:5),2)), +

Today's R-devel has a problem in `example(medpolish)'. It appears that the `plot.medpolish' method is not available. Here is the output and traceback() after starting `R --vanilla': > library(eda) > example(medpolish) mdplsh> deaths <- rbind(c(14, 15, 14), c(7, 4, 7), c(8, 2, 10), c(15, 9, 10), c(0, 2, 0)) mdplsh> dimnames(deaths) <-

Dear friends. I have these well known data on horsekicks: years <- c(109, 65, 22, 3 , 1, 0) deaths <- 0:5 and get a nice but wrong fit from summary(z1 <- glm(years~deaths,family=poisson)) Can I take away the intercept ? Best wishes Troels Ring, Aalborg -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Like most help forum users, I'm very new to R. I've been having this problem: I started with a dataframe called fullData. With the subset command, I split it into two separate dataframes, soloData and teamData. The hist() function works when I use... hist( subset(fullData, fullData$playlist_id==4 )$deaths) ...the exact call to subset() that I used to create each dataframe. However,

I'm having trouble figuring out how to format Date variables when used as axis labels in graphs. The particular case here is an attempt to re-create Nightingale's coxcomb graph with ggplot2, where I'd like the months to be labeled as "Mar 1885", "Apr 1885", using a date format of "%b %Y" applied to label the dates, or really anything other than

Consider the following example (based on an example in Pat Altham's GLM notes) pyears <- scan() 18793 52407 10673 43248 5710 28612 2585 12663 1462 5317 deaths <- scan() 2 32 12 104 28 206 28 186 31 102 Smoke <- gl(2,1,10,labels=c("No","Yes")) Age <- gl(5,2,10,labels=c("35-44","45-54","55-64","65-74","75-84"),

Hello, I'm working on using the LLVM JIT for a little project of mine, amazing work first off! I have a question about optimization passes. I initially have this function I've created, in python it looks like this: OS_end = 50OS_start = 0OS_timestep = 1birth_rate = .3population = 30.0for time in range(OS_start, OS_end, OS_timestep): births = birth_rate * population deaths = 0.1

Dear list, I am trying to set up a propensity-weighted regression using the survey package. Most of my population is sampled with a sampling probability of one (that is, I have the full population). However, for a subset of the data I have only a 50% sample of the full population. In previous work on the data, I analyzed these data using SAS and STATA. In those packages I used a propensity weight

We are using glm() to models to counts of deaths due to rare causes using a log link and Poisson error distribution, with population as the offset. Approximate confidence intervals for the parameter estimates are easy to calculate using a standard normal deviate, but obviously when the counts of deaths are small (which is why we are using Poisson regression), these intervals are very approximate

Dear list: I have tried demography package in my Ubuntu(10.04). What I interested in was lifetable analysis. Chiang(1984)'s reference have been copied to steeper my learning curve. step by step, the example presented by the textbook also have been examined. In the course I find that the paramter "fraction of last age intervl of life" could not be take as a option in the module

The expected number of bladder cancer over next 20 years a tire industry is 1.8. Poission distribution is assumed to hold and 6 reported deaths are caused by bladder cancer among the employees. Trying to find how unusual this event is. > ppois(q=6, lambda=1.8, lower.tail = TRUE, log.p = FALSE) [1] 0.9974306 not sure if ppois is the right one to use and the parameters... thx much

This was accidentally addressed to me, personally: ------- start of forwarded message ------- Message-id: <39F70D7C.287323D5 at yale.edu> MIME-version: 1.0 From: Diana Fischer <diana.fischer at yale.edu> To: maechler at stat.math.ethz.ch Subject: R-Project Date: Wed, 25 Oct 2000 11:42:36 -0500 I briefly looked at the R documentation of available statistical procedures. It appears

I have posted, at http://wwwmaths.anu.edu.au/~john/r/newsets/ image (.rda) files, and first stabs at .Rd files for various data on deaths in London from 1629 to 1939. (There are of course gaps.) The sources (Guy 1882 & Stocks 1942) are documented in the .Rd files: (1) poxetc: measles, smallpox & total deaths: 1629-1881 [I have deliberately left several inconsistencies that were in

Dear R People: Here is a little section of a data frame: > zzz[1:10,] DATE GENDER AGE Co DEATH1 3945 2009-04-16 M 24 Botulinic 23 3851 2009-04-16 M 35 Constitutional 23 8495 2009-04-16 F 49 Constitutional 27 10967 2009-04-16 F 47 Constitutional 28 11164 2009-04-16 F 26 Constitutional 28 15220