similar to: scatterplot function - double check: dashed lines

Hello everyone, (This is my second question posted today on the R list). I am carrying out a competing risks analysis using the cuminc function...this takes the form: cuminc(ftime,fstatus,group) In my study, fstatus has 3 different causes of failure (1,2,3) there are also censored cases (0). "group" has two levels (0 and 1). I therefore have 6 different cumulative incidence curves:

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

Hello. My name is Akashah. i work at metabolic laboratory. From my study, i found that volcano plot can help a lot in my section.  i already studied about the volcano plot and get the coding to run in R software, unfortunately, there is may be something wrong with the coding. This is because  no graph appear, but no error (blue color text) was shown on the R console. Below is the coding for

I posted several months about the problem with adding tick marks to curves using lines.survfit. This occurs when lines.survfit is used to add a curve to survival curves plotted with plot.survfit. The help for this function implies that mark.time=TRUE thus: plot(pfsfit,conf.int=FALSE,xscale=365.25,yscale=100,xlab="Years",ylab="% surviving",lty=2,mark=3)

Dear R-help list members, I am trying to create my own axes when plotting a cumulative incidence curve using the plot.cuminc function in the CMPRSK library. The default x-axis places tick marks and labels at 0, 20, 40, 60, and 80 (my data has an upper limit of 96), whereas I want them at my own specified locations. Here is my example code: library(cmprsk) attach(MYDATA) MYCUMINC <-

Hi All, I am running a scatter plot and trying to add a best fit line. I use an abline function, but get no line drawn over the points. I also get no error. I arm using V 2.10.0 on Windows 7. Here is my code, including the SAS transport file import: require (foreign) require (chron) require (Hmisc) require (lattice) clin <- sasxport.get("y:\\temp\\subset.xpt") attach(clin)

Dear R-users, I'm studing a DB, structured like this (just a little part of my dataset): _____________________________________________________________________________________________________________ Site Latitude Longitude Year Tot-Prod Total_Density dmp Dendoudi-1 15.441964 -13.540179 2005 3271.16 1007 16993.25 Dendoudi-2 15.397321 -13.611607

HELLO, anybody... could you help me to check the below coding for volcano. what is the mistake? what the plot could not display? # volcano_plot.r # # Author: Amsha Nahid, Jairus Bowne, Gerard Murray # Purpose: Produces a volcano plot # # Input: Data matrix as specified in Data-matrix-format.pdf # Output: Plots log2(fold change) vs log10(t-test P-value) # #

Hi All, I'm trying to figure out the cumulative incidence curve in R in some limited time. I found in package "cmprsk", the command "plot.cuminc" can get this curve. But I noticed that there is no mark for the censored time there, comparing with the KM curve by "plot.survfit". Here are my codes (attached is the data): ----------------

Hi, I am wondering if anyone can explain to me if cumulative incidence (CI) is just "1 minus kaplan-Meier survival"? Under what circumstance, you should use cumulative incidence vs KM survival? If the relationship is just CI = 1-survival, then what difference it makes to use one vs. the other? And in R how I can draw a cumulative incidence plot. I know I can make a Kaplan-Meier

Dear friends: I'm interested to make a stacked plot of cumulative incidence. that's, the cuminc model is fitted [fit=cuminc(time, relapse)] and cumulative incidence is in place. I'd like to stack the cuminc plots (relapse of luekemia and death free from leukemia, for example) , then the constituent ratio of leukemia relapse and treatment related mortality is very clear. Can

Dear R community In a current paper, I'm (briefly) considering the topic of producing scatterplots or maps with point labels positioned in such a way as to minimize label overlap and occlusion. This is a topic with a large, but scattered literature. In CS, it is considered NP-hard, but there are a variety of approximate solutions. The most complete bibliography I've found is the

I am trying to make a scatterplot with containing three columns. I know how to plot the two columns but now the third column consists of M or F (male or female) and I don't know how to separate the data so I can make two separate regression lines on the same graph. meta #name of file plot(meta$mass, meta$rate, xlab="mass", ylab="rate") I can make the regression line

Hi Folks, Please, when I ask the option reg.line at the scatterplot in package car, the OLS models includes a constant? If not how can I do it sing the following code: scatterplot(lfirms ~ lscale, data=dataset, reg.line=lm, smooth=FALSE, labels=FALSE, span=0.5, xlab="Relative Plant Fixed Cost", ylab="Relative Number of Firms", pch=c(18),

(I have checked the 2000 &2001 archives) Awhile ago, someone posted a function to plot a scatterplot matrix with the upper triangle containing plots (and maybe fitted curves?) and the lower triangle containing the correlation coefs. I have lost that code and would love it again if anyone has it. Thanks, Henry ***************************** Martin Henry H. Stevens HStevens at muohio.edu tel:

I know that by setting alpha to for example col = rgb(0, 0, 0, 0.1) it is possible to see how many overlapping is in the plot. But disadvantage of it is that single points are barely visible on the background. So I wonder if there is possible to make setting that single points would be almost black, but with more and more data on the same spot it would get more and more whiteish. Or maybe it is

I have attempted to follow posting guidelines but I have failed to find out what I am doing wrong here. I am trying to use lines.survfit to plot a second curve onto a survival curve produced by plot.survfit. In my case this is to be a progression free survival curve superimposed upon an overall survival curve, but I will illustrate my problem using the example given in the help for

Hi. I would like to make a scatterplot where all of the points are evenly spaced from each other - however, they are all the same size and occupy the entire graph. For example: x = rep(c(1:10), 10) y = rep(c(10:1), each = 10) plot(x, y, pch = 0) Gives me a scatter plot with 100 square points each evenly spaced between each other. But these points don't fill up the entire space and if I try

Dear Prof Gray and everyone, As our package developers discussed about incompatibility between Design and survival packages, I faced another problem with cmprsk- a survival dependent packacge. The problem is exactly similar to what happened to the Design package that when I just started running cuminc function, R was suddenly closed. These incidents suggest that maybe many other survival

Hello I have the following data.frame and xyplot. I need this plot without the borders. Does anybody know how to get this xyplot without borders o with white borders? Thank's in advance Juan Hernández my.label <- data.frame(quantil=rep(20,8), my.factor=factor(c('FA','FB','FC','FD','FE','FG','FH','FI'))) library(lattice)