similar to: Greater than less than in "ifelse"

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Dear R Users, Could you please help me on the following issue? I have a real large yearly data set. For each year I have 365 flow values. Some of the flow values are not known and that’s why you will see NA written in those slots. I wanted to know, is there a way that I can estimate those values? I tried approx command but it seems least helpful for the kind of issue I am up against.

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

Hello R-community, It's been a week now that I am struggling with the implementation of a cox model in R. I have 80 cancer patients, so 80 time measurements and 80 relapse or no measurements (respective to censor, 1 if relapsed over the examined period, 0 if not). My microarray data contain around 18000 genes. So I have the expressions of 18000 genes in each of the 80 tumors (matrix

Dear arun and all R users, I will first of all try to simply define my issue.. I have data in the following format Year Discharge dd/mm/yyyy x .. … … … There are some NA values in the discharge which I would like to predict by using “predict command”. I cant figure out the way to write the coding for that. Could you please help me on that??? I have also ,written

Dear list, I am not familiar with survival analysis and I would need your help to understand a result I have obtained. I have used the following command line to look at number of events and probability of survival at the first 5 years: > su = summary(survfit(Surv(a[, Date], a[, Event]) ~ strata(a[,Prediction]), data = a), times=c(0,1,2,3,4,5)) I have studied two kind of events (disease-free

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that ?).

Hello, I have a problem regarding calculation of Cumulative Incidence Function. The event of interest is failure of bone-marrow transplantation, which may occur due to relapse or death in remission. The data set that I have consists of- lifetime variable, two indicator variables-one for relapse and one for death in remission, and the other variables are donor type (having 3 categories), disease

I have written a wrapper for heatmap.2 called heatmap.w.row.and.col.clust which auto-generates breaks using breaks<-round((c(seq(from=(-20 * stddev), to=(20 * stddev))))/20, digits = 2) #(stddev in this case = 2.5) This has always worked well in the past but now I am getting an error that non-finite breaks are being generated. Drilling down, it seems that my wrapper is generating finite

Hi Bert, I am sorry to bother you on weekend. I am still struggling on defining a correct function. I first defined the function RFS (see below), then run it by provide the two argument. m52.2cluster <-RFS(inputfile =allinfo_m52, N=2 ) I do not get error message, but no figure displays on screen. I do not know what is going on. Can you help me a little more on this issue? Thank you,

I have a dataset that I'm trying to rearrange for a repeated measures analysis: It looks like: patient basefev1 fev11h fev12h fev13h fev14h fev15h fev16h fev17h fev18h drug 201 2.46 2.68 2.76 2.50 2.30 2.14 2.40 2.33 2.20 a 202 3.50 3.95 3.65 2.93 2.53 3.04 3.37 3.14 2.62 a 203 1.96 2.28 2.34 2.29 2.43 2.06 2.18 2.28 2.29 a

Hi Richard, Thank you so much!! I understand the problem now, I assign a name to the "ggsurvplot" object and then add print(fig) at bottom of function definition, now figure gets printed on screen. Ding # function to generate RFS curves RFS <- function( inputfile, N ) { cluster<- survfit(Surv(RFS_days2, OV_Had_a_Recurrence_CODE) ~ clusters, data =

Hello R users, I am trying to run a cox model for the prediction of relapse of 80 cancer tumors, taking into account the expression of 17000 genes. The data are large and I retrieve an error: "Cannot allocate vector of 2.4 Mb". I increase the memory.limit to 4000 (which is the largest supported by my computer) but I still retrieve the error because of other big variables that I have in

FAQ 7.22 You must print a ggplot object, for example with print(m52.2cluster) For the FAQ, run the line system.file("../../doc/FAQ") in R on your computer. Open up the resulting filepath in your favorite editor and scroll down to 7.22 On Sun, Jan 14, 2018 at 4:21 PM, Ding, Yuan Chun <ycding at coh.org> wrote: > Hi Bert, > > I am sorry to bother you on weekend. >

That is certainly OK, but you can also just use print(ggsurvplot(...)) as your final statement. out <- RFS( ...) would then return the ggsurvplot object *and* graph it. Any good R tutorial or a web search will provide more details on function returns, which you might find useful. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and

Dear list, I am trying to build a cox model and then perform ROC analysis in order to retrieve some genes that are correlated with breast cancer. When I calculate the hazard score taking into account different numbers of genes and their coefficients ( I am trying to find the pest predictor number of genes), I retrieve from around 1 values (for few genes included ) to size of e+80 values (for many

Thank you, your suggestion is simpler and logically better. I had impression that the last object in a function gets returned, so I did not add the print function at the bottom line of the function definition. Returning an object and graph the object are different process, I am a beginner for writing R function and need to find a good guide source about writing R functions. If you know a good

For any given pre-specified gene or short list of genes, yes the Cox model works fine. Two important caveats: 1. Remeber the rule of thumb for a Cox model of 20 events per variable (not n=20). Many microarray studies will have very marginal sample size. 2. If you are looking at many genes then a completely different strategy is required. There is a large and growing literature; I like Newton

Dear all, I have been given a data something like below: Dat = "2 3 28.3 3.05 8 3 3 22.5 1.55 0 1 1 26.0 2.30 9 3 3 24.8 2.10 0 3 3 26.0 2.60 4 2 3 23.8 2.10 0 3 2 24.7 1.90 0 2 1 23.7 1.95 0 3 3 25.6 2.15 0 3 3 24.3 2.15 0 2 3 25.8 2.65 0 2 3 28.2 3.05 11 4 2 21.0 1.85 0 2 1 26.0 2.30 14 1 1 27.1 2.95 8 2 3 25.2 2.00 1 2 3 29.0 3.00 1 4 3 24.7 2.20 0 2 3 27.4 2.70 5 2 2 23.2 1.95

Hi Peter, the "residuals()" function returns the residuals of a model fitted using the "lm" function. For instances, using the example included in the help of lm: ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2, 10, 20, labels = c("Ctl","Trt")) weight <-