similar to: Fitting a survival curve

Is there a way of repositioning the y label in scatterplot3d so that it is parallel with the y axis? Many thanks Richard

Dear All, reposting, because I did not find a solution, maybe someone could check the example below. It's taken from the help page of survdiff. Executing it, gives the error "Error in floor(temp) : Non-numeric argument to mathematical function" best regards, Heinz library(survival) ## Example from help page of survdiff ## Expected survival for heart transplant patients based

I am using the following code: library(lattice) data<-read.csv("data.csv") attach(data) fig<-xyplot(S_t~month|event, key = list(text=list(lab=c("Time to first CV event - Data", "Survival post first CV event - Model", "Survival post first MIA/CA event - Data",

Hi all, I can't find the error in the binomial GLM I have done. I want to use that because there are more than one explanatory variables (all categorical) and a binary response variable. This is how my data set looks like: > str(data) 'data.frame': 1004 obs. of 5 variables: $ site : int 0 0 0 0 0 0 0 0 0 0 ... $ sex : Factor w/ 2 levels "0","1": NA NA NA

Dear all, I'm trying to fit a curve to some 1 year failure-time data, so that I can extrapolate and predict failure rates up to 3 years. The data is in the general form: Treatment Time Status Treatment A 28 0 Treatment B 28 0 Treatment B 28 0 Treatment A 28

OK this is bound to be something silly as I'm completely new to R - having started using it yesterday. However I am already warming to its lack of 'proper' GUI... I like being able to rerun a command by editing one parameter easily... try and do that in a Excel Chart Wizzard! I eventually want to use it to analyse some chemotherapy response / survival data. That data will not be

Dear R users, Please can you help me with a relatively straightforward problem that I am struggling with? I am simply trying to plot a baseline survivor and hazard function for a simple data set of lung cancer survival where `futime' is follow up time in months and status is 1=dead and 0=alive. Using the survival package: lung.wbs <- survreg( Surv(futime, status)~ 1, data=lung,

Hi I am trying to determine the mean of a Weibull function that has been fit to a data set, adjusted for a categorical covariate , gender (0=male,1=female). Here is my code: library(survival) survdata<-read.csv("data.csv") ##Fit Weibull model to data WeiModel<-survreg(Surv(survdata$Time,survdata$Status)~survdata$gender) summary(WeiModel) P<-pweibull(n,

Deal list, I have a data frame (birth) with mixed variables (numeric and alphanumeric). One variable "t1stvisit" was originally coded as numeric with values 1,2, and 3. After attaching the data frame, this is what I see when I use str(t1stvisit) $ t1stvisit: int 1 1 1 1 1 1 1 1 2 2 ... This is as expected. I then convert t1stvisit to a factor and to avoid creating a second

Hi, I have only the vaguest notions of what parallel programing, but I think I have a situation where it might be of use to me, or at least provide me with the opportunity to learn more about it. Before I invest in figuring out the nuts and bolts, can anyone confirm that this is a sane approach, or provide alternatives that I could pursue? I'm running stochastic simulations, with the actual

Dear R users, This is a basic question. I want to fit a Weibull distribution. fitdistr(data, "weibull") works and it is a maximum likelihood fitting. Is it a good method ? Or is it better to write a function for the log-likelihood and the gradient and to use a numerical routine ? Fitdistr works for uncensored data, but what can I use for censored (and uncensored) data ? Thank you

Hi All, I have data on the sequence of births for families with completed fertility cycle (in a data frame); the relevant variables are called b1, b2, b3, b4, b5, b6 and record the birth of the first, second, ..., sixth child. So, b1=1 if the first birth is male, b1=2 if the first birth is female, and b1=NA if the family did not record any first birth. Similarly for b2, b3, b4, b5 and b6. I

Hi all, Rails 2.1 seems to converts the following parameters {''birth(1i)''=''1990'', ''birth(2i)''=>''2'', ''birth(3i)''=>''31''} into date ''1990-03-02'' automatically. Is it able to inhibit this convertion? -- makoto kuata

Hello, I am interested in producing the expected number of events, in a recurring events setting. I am using the Andersen-Gill model, as fit by the function "coxph" in the package "survival." I need to produce expected numbers of events for a cohort, cumulatively, at several fixed times. My ultimate goal is: To fit an AG model to a reference sample, then use that fitted model

Dear R users, I am having trouble devising an efficient way to run a loess() function on all columns of a data.frame (with the x factor remaining the same for all columns) and I was hoping that someone here could help me fix my code so that I won't have to resort to using a for loop. (You'll find the upcoming lines of code in a single block near the end of the message.) Here's a

Dear all, I have a couple of questions regarding the survival:::cch function. 1) I notice that Prentice and Self-Prentice functions are giving identical standard errors (not by chance but by programming design) while their estimates are different. My guess is they are both using the standard error form from Self and Prentice (1986). I see that standard errors for both methods are

Hi all, I want to make a contingency table in R. I want to tabulate two variables, one as the independent and second as the dependent variable. The IV has two categories, namely, birth complications, and no birth complications. The frequency of birth complication category is fifty, and the frequency of no birth complication category is 34. The categories and frequencies of DV follows.

http://r.789695.n4.nabble.com/file/n3389613/Life_Expectancies_2008.csv Life_Expectancies_2008.csv I am trying to plot a histogram base on the file i uploaded above. I am facing a trouble in sorting out the frequency of the life expectancies. I wanted to plot a graph of life expectancies at birth versus frequency,but i have no idea how to change the locations into frequencies taking the range of

Hi All, I'm trying to delete a row from my dataframe "pop" without changing the indexing (column 0) as follows: >pop id birth size xloc yloc weight energy gonad consumed 1 1 36 13 34 43 0 18 0 0 2 2 36 10 39 38 0 18 0 0 3 3 36 10 37 35 0 18 0 0 4 4 36 10 31 25 0

Dear R gurus, I have the following code, but I still not know how to estimate and extract confidence intervals (95%CI) from resampling. Thanks! ~Adriana #data penta<-c(770,729,640,486,450,410,400,340,306,283,278,260,253,242,240,229,201,198,190,186,180,170,168,151,150,148,147,125,117,110,107,104,85,83,80,74,70,66,54,46,45,43,40,38,10) x<-log(penta+1) plot(ecdf(x),