similar to: Survival and nonparametric

Dear all, I am looking for a good reference on "Survival analysis". I am looking for a booking containing both applications and Maths. Explaining different methods in survival analysis .... Many thanks Bernard --------------------------------- [[alternative HTML version deleted]]

For time scale that are truly discrete Cox proposed the "exact partial likelihood". I call that the "exact" method and SAS calls it the "discrete" method. What we compute is precisely the same, however they use a clever algorithm which is faster. To make things even more confusing, Prentice introduced an "exact marginal likelihood" which is not

Hi all, Is there an equivalent to predict(...,type="linear") of a Proportional hazard model for a Cox model instead? For example, the Figure 13.12 in MASS (p384) is produced by: (aids.ps <- survreg(Surv(survtime + 0.9, status) ~ state + T.categ + pspline(age, df=6), data = Aidsp)) zz <- predict(aids.ps, data.frame(state = factor(rep("NSW", 83), levels =

Dear R users I would like to calculate the Cumulative incidence for an event adjusting for competing risks and adjusting for covariates. One way to do this in R is to use the cmprsk package, function crr. This uses the Fine & Gray regression model. However, a simpler and more classical approach would be to implement the Kalbfleisch & Prentice method (1980, p 169), where one fits cause

Dear R-users, I am a new user for R. I am eager to lean about it. I wanted to read and summary of the a simple data file I used the following, rel <- read.table("C:/Documents and Settings/ashta/My Documents/R_data/rel.dat", quote="",header=FALSE,sep="",col.names= c("id","orel","nrel")) summary(rel) Below is the

Hi Ashta, There are many ways to do it. Here is one: vars <- sapply(split(DM$x, DM$GR), var) DM[DM$GR %in% names(vars[vars > 0]), ] Best Ista On Wed, Dec 6, 2017 at 6:58 PM, Ashta <sewashm at gmail.com> wrote: > Thank you Jeff, > > subset( DM, "B" != x ), this works if I know the group only. > But if I don't know that group in this case "B", how

Hello, Try the following. keep <- list(A = c(15, 30), B = c(40, 50), C = c(60, 75)) sp <- split(DM$x, DM$GR) inx <- unlist(lapply(seq_along(sp), function(i) keep[[i]][1] <= sp[[i]] & sp[[i]] <= keep[[i]][2])) DM[inx, ] # GR x y #1 A 25 125 #2 A 23 135 #5 B 45 321 #6 B 47 512 #9 C 61 521 #10 C 68 235 Hope this helps, Rui Barradas On 12/9/2017 12:48 AM, Ashta

> On Dec 8, 2017, at 4:48 PM, Ashta <sewashm at gmail.com> wrote: > > Hi David, Ista and all, > > I have one related question Within one group I want to keep records > conditionally. > example within > group A I want keep rows that have " x" values ranged between 15 and 30. > group B I want keep rows that have " x" values ranged

Hi David, Ista and all, I have one related question Within one group I want to keep records conditionally. example within group A I want keep rows that have " x" values ranged between 15 and 30. group B I want keep rows that have " x" values ranged between 40 and 50. group C I want keep rows that have " x" values ranged between 60 and 75. DM <-

Thank you Ista! Worked fine. On Wed, Dec 6, 2017 at 5:59 PM, Ista Zahn <istazahn at gmail.com> wrote: > Hi Ashta, > > There are many ways to do it. Here is one: > > vars <- sapply(split(DM$x, DM$GR), var) > DM[DM$GR %in% names(vars[vars > 0]), ] > > Best > Ista > > On Wed, Dec 6, 2017 at 6:58 PM, Ashta <sewashm at gmail.com> wrote: >> Thank

I am trying to plot a line graph for 3 or more regression lines abline(m1) abline(m2) abline(m3) Can I change the color of each line? if so how? Thanks in advance Ashta [[alternative HTML version deleted]]

> On Dec 6, 2017, at 4:27 PM, Ashta <sewashm at gmail.com> wrote: > > Thank you Ista! Worked fine. Here's another (possibly more direct in its logic?): DM[ !ave(DM$x, DM$GR, FUN= function(x) {!length(unique(x))==1}), ] GR x y 5 B 25 321 6 B 25 512 7 B 25 123 8 B 25 451 -- David > On Wed, Dec 6, 2017 at 5:59 PM, Ista Zahn <istazahn at gmail.com> wrote:

library(dplyr) DM <- read.table( text='GR x y A 25 125 A 23 135 . . . ) DM %>% filter((GR == "A" & (x >= 15) & (x <= 30)) | (GR == "B" & (x >= 40) & (x <= 50)) | (GR == "C" & (x >= 60) & (x <= 75))) On Fri, Dec 8, 2017 at 4:48 PM, Ashta <sewashm at gmail.com>

> On Dec 8, 2017, at 6:16 PM, David Winsemius <dwinsemius at comcast.net> wrote: > > >> On Dec 8, 2017, at 4:48 PM, Ashta <sewashm at gmail.com> wrote: >> >> Hi David, Ista and all, >> >> I have one related question Within one group I want to keep records >> conditionally. >> example within >> group A I want keep rows that

You could make numeric vectors, named by the group identifier, of the contraints and subscript it by group name: > DM <- read.table( text='GR x y + A 25 125 + A 23 135 + A 14 145 + A 35 230 + B 45 321 + B 47 512 + B 53 123 + B 55 451 + C 61 521 + C 68 235 + C 85 258 + C 80 654',header = TRUE, stringsAsFactors = FALSE) > > GRmin <- c(A=15, B=40, C=60) > GRmax <-

Hi, I want to autoload ide-scsi.o on each boot. In debian there is a file /etc/modules in which I can iclude the names of the modules that are to be loaded at boot time As I remember in gentoo there was a similar /etc/modules.auto or something ... is there an appropriate place in a redhat distribution or I should write my own rc script? sasoon

I am trying to model data in which subjects are followed through time to determine if they fall, or do not fall. Some of the subjects fall once, some fall several times. Follow-up time varies from subject to subject. I know how to model time to the first fall (e.g. Cox Proportional Hazards, Kaplan-Meir analyses, etc.) but I am not sure how I can model the data if I include the data for those

Dear R-users, Suppose I have the following sample of data, 0 1 2 4 3 1 2 1 3 1 1 3 3 4 1 0 1 2 1 2 1 4 1 4 2 1 2 2 1 1 The first variable is the response variable where 0 is defective and 1 normal. The other four factors( x1,x2,x3,x4) that influence the outcome. I want to fit a binomial model . How do I do that? I am guessing the response variable

Hi all, I have a data set called x with 200 rows and 12 columns. I want create two more columns based on probability. ie if p >0 .4 then v1 =1 else v1=0; if p >0 .6 then v2 =1 else v2=0; Finally x will have 14 variables. Can any one show me how to do that? Thanks Ashta . [[alternative HTML version deleted]]

Thank you Jeff, subset( DM, "B" != x ), this works if I know the group only. But if I don't know that group in this case "B", how do I identify group(s) that all elements of x have the same value? On Wed, Dec 6, 2017 at 5:48 PM, Jeff Newmiller <jdnewmil at dcn.davis.ca.us> wrote: > subset( DM, "B" != x ) > > This is covered in the Introduction to