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I have a data with the sequence of events with millions records and more than 24 time stamped variables. sample data: 1 2 3 4 5 6 7 8 9 10 A A A C C C B B D D D D D D D C C C C C B B A C A C C C D D I want to predict sequence for next 3 period. How this can be achieved using R. Is there any package/function in R do this. I have gone through the document for package "TraMineR" but not

Hi List, I want to read several parameters from data frame and load them as object into R session, Is there any package or function in R for this?? Here is example param <-c("clust_num", "minsamp_size", "maxsamp_size", "min_pct", "max_pct") value <-c(15, 20000, 200000, 0.001, .999) data <- data.frame ( cbind(param , value)) data

Hi Experts, I am new to R, using following sample code for capping outliers using percentile information. Working on large data (30000 observations and 150 variables), loop I am using in the below mentioned code for detecting outliers and capping to upper /lower percentile value is taking much time for the execution. Is there anything wrong with code, can anyone suggest improvement in the script

...tuationy=number of particles emitted in 1 hr period~pois(30)p=probability of detection of radiation particlesx=number of particles detected by a radiation detector~pois(30p)where p~beta(a,1)I have to calculate the loglikehood for a for the range a(2,50)I wish to simulate 100 random samples for each aHere is my code:-m=481n=100x = c(15, 36, 29, 28, 37, 32, 25, 27, 31, 21, 25, 27, 28, 31, 28, 20, 34, 25, 20, 34, 15,21, 28, 24, 31, 19, 34, 29, 18, 25, 16, 19, 44, 26, 34, 31, 21, 28, 11, 31, 21, 34, 25, 25,30, 23, 21, 35, 36, 21, 27, 29, 30, 22, 25, 30, 24, 27, 28, 22, 36, 29, 33, 35, 30, 32, 27,26, 25...

n <- 10 P1 <- runif(n) P2 <- runif(n) P3 <- P1 + P2 + runif(n)/100 P4 <- P1 + P2 + P3 + runif(n)/100 mydata <- data.frame(cbind(P1,P2,P3,P4)) mydata[1,1] <- 8 mydata[3,1] <- -5 mydata[2,3] <- -6 mydata[7,3] <- 7 f=function(z){quantile(z, c(0.01, 0.99)) } temp1 <- lapply(mydata, f) temp1 $P1 1% 99% -4.542391 7.354209 $P2 1% 99%

Dear Members, I required to score probability of survival before specified time using fitted cox model on scoring dataset. On the training sample data I am able to get the probability of a survival before time point(t), but on the scoring dataset, which will have only predictor information I am facing some issues. It would be great help for me if you tell me where am I going wrong! Here is the