search for: deaths

I came across this one: http://www.nysun.com/article/32787 which says that the violent death rate in Iraq (which presumably includes violent deaths from the war) is lower than the violent death rate in major American cities. Does anyone have any insights from statistics on how to interpret this?

...the calc is to be made but I can't figure out how to tell R to take the current value and divide it by the next value. The formula is P0=L1/LO The following is an example of the lifetable I'm constructing: example=as.vector(c(8,2,1,5,6,7,7,0,8,10,13,8,11,11,11,2,7,1,5,6,8,6)) #prime k=1 #Deaths# deaths=numeric(k) for(k in 0:max(example)) { deaths[k]=sum(example==k)} #adjust for no zero!!# deaths=c(0,deaths) #Alive, Kx# alive=sum(deaths)-cumsum(deaths) #Day, or age class,x# day=seq(from=0,to=length(deaths)-1) #mortality, lx# lx=alive/sum(deaths) #proportion surviving to next interval, px...

There seems to be a new regression in current llvm/compiler-rt 3.2 branch. On 86_64-apple-darwin12, I am seeing the failure... [100%] Running all regression tests lit.py: lit.common.cfg:19: note: using clang: '/sw/src/fink.build/llvm32-3.2-0/llvm-3.2/build/bin/clang' lit.py: lit.cfg:171: note: using clang: '/sw/src/fink.build/llvm32-3.2-0/llvm-3.2/build/bin/./clang' FAIL:

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 please help. I am searching the web 5 days n...

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 condition, it selects the region I want. However, when I input kills = data$kills[data$deaths>20 && data$deaths<=27] "kills" becomes the entire vector data$kills, wit...

Hi everyone, I am not new to R, but new to running survival models in R. I am trying to create some basic KM curves, using the following code: library(survival) library(KMsurv) (import data etc - basic right censored, with continuously observed time of death) sleepfit <- survfit(Surv(timeb, death), data = sleep) Here timeb is measured is survival in years, death is a 1/0 indicator (1 =

...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 model, Gompertz, and a probability distribution, Poisson). Then a multiply both deaths and exposures by a constant. Finally I fitted with glm the simulated data sets and I compared the deviance residuals. They're different by a certain constant, but I could not get a meaningful relatio...

We are running OCFS2 on SLES9 machines using a FC SAN. Without warning both nodes will become unresponsive. Can not access either machine via ssh or terminal (hangs after typing in username). However the machine still responds to pings. This continues until one node is rebooted, at which time the second node resumes normal operations. I am not entirely sure that this is an OCFS2 problem at all

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 http://www.ci.tuwien.ac.at/~h...

...e 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) >deaths=c(0,0,2,0,0,1,6) where deaths is the number of deaths on a given day. Now I need to create a new vector with the number alive for each day and this is where I'm stuck... I've tried to work various for and while loops but haven't had success. The vector should be: >Alive=c(9,9,7,7,...

Hi all, I'm using slackware 10, running samba-3.0.7, OpenLDAP-2.2.17 with ssl, tls and sasl2. While i trying include a workstation windows xp in the samba domain it return this: [2004/10/05 12:51:25, 0] lib/util_sock.c:send_smb(647) Error writing 4 bytes to client. -1. (Connection reset by peer) [2004/10/05 12:51:26, 0] lib/util_sock.c:get_peer_addr(1000) getpeername failed.

Dear everyone, My question involves the use of the survival object. We can have Surv(time,time2,event, type=, origin = 0) (1) As detailed on p.65 of: http://cran.r-project.org/web/packages/survival/survival.pdf My data (used in my study) is 'right censored' i.e. my variable corresponding to 'event' indicates whether a person is alive (0) or dead (1) at date last seen

On a fresh Ubuntu, sudo apt-get install wine wine iexplore yields a white screen of death with a title bar for Wine. I tried winecfg and tweaking settings, and I can get a desktop with IE WSOD again, so no real progress. The shell outputs a ton of errors/debug info, pasted below... What have I done wrong? wine iexplore fixme:ole:CoResumeClassObjects stub fixme:shdocvw:go_home stub

Dear all, I'm struggling with predicting "expected time until death" for a coxph and survreg model. I have two datasets. Dataset 1 includes a certain number of people for which I know a vector of covariates (age, gender, etc.) and their event times (i.e., I know whether they have died and when if death occurred prior to the end of the observation period). Dataset 2 includes another

Hello, I would like to use the spatstat package to generate realizations from a spatial birth death process. The death rate is dependent on the pairwise interaction function. 1 - I'd like to know how to define my own interaction potential function 2 - I'm not sure which model to use for a birth death process (my parameters are the birth and death rates) Any help would be greatly

Since I read the list in digest form (and was out ill yesterday) I'm late to the discussion. There are 3 steps for predicting survival, using a Cox model: 1. Fit the data fit <- coxph(Surv(time, status) ~ age + ph.ecog, data=lung) The biggest question to answer here is what covariates you wish to base the prediction on. There is the usual tradeoff between too few (leave out something

Dear R users, using dotplot (R1.2.0, WinNT4.0), I am trying to change the character size of the labels of the points: > # example > data(VADeaths) > dotplot(VADeaths, main = "Death Rates in Virginia - 1940") > # I'd like to have smaller character size of the labels (for age and population groups) > ?dotplot > # for argument "cex", this says: "Setting cex to a value smaller than one can be a useful way...

...per limits of the age-range categories (typically used by paleodemographers), and the third column is the number of skeletons assigned to each group. I've co-opted some R script for fitting a Gompertz-Makeham hazard model to this data... ############################## GM.naga <- function(x,deaths=naga) { a2=x[1] a3=x[2] b3=x[3] shift<-15 nrow<-NROW(deaths) S.t<-function(t) { return(exp(-a2*(t-shift)+a3/b3*(1-exp(b3*(t-shift))))) } d<-S.t(deaths[1:nrow,1])-S.t(deaths[1:nrow,2]) obs<-deaths[,3] lnlk<-as.numeric(crossprod(obs,log(d)))...

Hello all, I am hoping to use survival analysis to examine whether parasite attack increases nest death in a species of social wasp. I therefore have data for 1. Whether the nest "died" in the 6 week census period ("Status", where 1=died, 0=survived) 2. The day number of death/last recorded day it was observed alive. 3. Whether the nest was attacked by the parasite (0/1 as

Hello, I am using odesolve to simulate a group of people moving through time and transmitting infections to one another. In Matlab, there is a NonNegative option which tells the Matlab solver to keep the vector elements of the ODE solution non-negative at all times. What is the right way to do this in R? Thanks, Jeremy P.S., Below is a simplified version of the code I use to try to do this,