similar to: Root-Mean-Square(RMS) Difference

Dear R helper, I encountered a problem when I tried to plot the cumulative failure rate (i.e. 1 - survival probability). I have used the following code to plot. The scenario is that patients are randomized to different treatment arm (rev in the code), the PCI revascularization was monitored over 5 years. #R code testfit <- survfit(Surv(pcifu,pci)~rev,data=subproc) testfit$surv <- 1 -

Dear 'R' Users, I have CO2 timeseries which I want to fit with fourth harmonic function and then would like to compute residuals. I tried with two option but not sure which one is correct, kindly any one can help correcting me: # #I would like to fit a model with Fourth harmonic function as:

I have found some "issues" (bugs?) with nls confidence intervals ... some with the relatively new "port" algorithm, others more general (but possibly in the "well, don't do that" category). I have corresponded some with Prof. Ripley about them, but I thought I would just report how far I've gotten in case anyone else has thoughts. (I'm finding the code

Hello 'R' Users, I am very new on 'R', so excuse me if I ask something wrong. I have ASCII data and the colums of the data are looks like :- !------------------------- time,yr,mo,dy,hr,min,sec,lat,lon,ht,co2obs,sigma,co2model -- - -- !---------------------------- Each column has data value. Now I want to produce 2-d color maps, for example the plot should look like :- on

Hello all, I am trying to understand how to specify nested factors when using coxph(), and if it is appropriate to nest these factors in my situation. In the simplest form, I am testing two different temperatures, with each temperature being performed twice in different experimental periods (e.g. Temp5 performed in Period A and C, Temp4 performed in Period B and D) I am trying to see if survival

Hello users, I would like to obtain a survival curve from a Cox model that is smooth and does not have zero differences due to no events for those particular days. I have: > sum((diff(surv))==0) [1] 18 So you can see 18 days where the survival curve did not drop due to no events. Is there a way to ask survfit to fit a nice spline for the survival?? Note: I tried survreg and it did not

All I want to do is find the objects that were created today under the ''created_on'' column. I''ve tried many ways, here is one example using the chronic gem. @products = Product.find_all_by_created_on(Chronic.parse(''today'')) I must be missing something right in front of me .... Thanks for any help.

Dear R guru, I am Saikat Sarkar working as a researcher of Economics in Tampere University, Finland. I am trying to estimate some Garch related tests with Bayesian analysis by R programme. I am not good in R but trying to survive. Anyway I have the coding but not working properly. I have tried to find the problem but failed. I am writing to all R gurus to help me out. Could you please look at