similar to: time series fiting and residual computing

Dear R users, I am comparing two data sets (CO2 observation vs. CO2 simulation, during 1993-2002). In order to do it I am calculating Root-Mean-Square(RMS) difference with following formula: > sqrt(sum((observed_residual - simulated_residual)^2)/n) # 'n' is number of observations Residuals are computed by fitting a harmonic function on both the data:

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 -

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

Is there any R package that can fit a beta distribution in R? -- Thanks, Jim. [[alternative HTML version deleted]]

Dear R Users, I have a CO2 time series. I want to fit this series seasonal cycle and trend with fourth harmonic function, and then compute residuals. I am doing something like: file<-read.csv("co2data.csv") names(file) attach(file) fit<-lm(co2~1+time+I(time^2)+sin(2*pi*time)+cos(2*pi*time)+sin(4*pi*time)+cos(4*pi*time)+

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

Hello R Users, I am new to R and I have simple problem for R users. I have CO2 observations defined on time axis(yr,mo,day,hr,min,sec). (DATA ATTACHED HERE) First I want to convert time axis as one axis as 'hour' on regular interval as 1 hour. Say 00 hrs to 24hrs(jan1), 25hrs to 48hrs(jan2) and so on. Then I want to interpolate CO2 at every hour. Kindly anybody can help, Many thanks,