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...;,"2010-","2012-","1984-1992","1996-","2015-") > splitends<-sapply(end,strsplit,"-") > last_bit(x) return(x[length(x)]) > sapply(splitends,last_bit) > > Jim > > On Mon, Apr 25, 2016 at 4:35 PM, Sunny Singha > <sunnysingha.analytics at gmail.com> wrote: >> Hi, >> I have a char vector with year values. Some cells have single year >> value '2001-' and some have range like 1996-2007. >> I need to remove hyphen character '-' from all the values within the >> character vect...

...uot;2013-", "1993-2007","2010-","2012-","1984-1992","1996-","2015-") splitends<-sapply(end,strsplit,"-") last_bit(x) return(x[length(x)]) sapply(splitends,last_bit) Jim On Mon, Apr 25, 2016 at 4:35 PM, Sunny Singha <sunnysingha.analytics at gmail.com> wrote: > Hi, > I have a char vector with year values. Some cells have single year > value '2001-' and some have range like 1996-2007. > I need to remove hyphen character '-' from all the values within the > character vector named as 'end...

Hi, I have a char vector with year values. Some cells have single year value '2001-' and some have range like 1996-2007. I need to remove hyphen character '-' from all the values within the character vector named as 'end'. After removing the hyphen I need to get the last number from the cells where there are year range values i.e if the cell has range 1996-2007, the code

...representing what you have (dput() is handy for this) and one representing what you want to have with any guidance regarding how to use the other columns in you data set (e.g., the event(0/1)). Mark R. Mark Sharp, Ph.D. msharp at TxBiomed.org > On Jul 4, 2017, at 7:02 AM, Sunny Singha <sunnysingha.analytics at gmail.com> wrote: > > Thanks Boris and Bret, > I was successful in simulating granular/transactional data. > Now I need some guidance to transform the same data in format acceptable > for survival analysis i.e below format: > > pump_id | event_episode_no. | even...

...y for this) and one representing what you want to have with any guidance regarding how to use the other columns in you data set (e.g., the event(0/1)). > > Mark > R. Mark Sharp, Ph.D. > msharp at TxBiomed.org > > > > > >> On Jul 4, 2017, at 7:02 AM, Sunny Singha <sunnysingha.analytics at gmail.com> wrote: >> >> Thanks Boris and Bret, >> I was successful in simulating granular/transactional data. >> Now I need some guidance to transform the same data in format acceptable >> for survival analysis i.e below format: >> >> pump_i...

I apologise as I had mistakenly posted this message via non- member mail. So I'm reposting it with member id. I need help in this case. > Hi friends, > I haven't done such a simulation before and any help would be greatly appreciated. I need your guidance. > > I need to simulate end to end data for Reliability/survival analysis of a Pump ,with correlation in place, that is

In principle what you need to do is the following: - break down the time you wish to simulate into intervals. - for each interval, and each failure mode, determine the probability of an event. Determining the probability is the fun part, where you make your domain knowledge explicit and include all the factors into your model: cumulative load, failure history, pressure, temperature,

Thanks Boris and Bret, I was successful in simulating granular/transactional data. Now I need some guidance to transform the same data in format acceptable for survival analysis i.e below format: pump_id | event_episode_no. | event(0/1) | start | stop | time_to_dropout The challenge I'm experience is to generate the 'start' and 'stop' in units of minutes/days from single

Hi, I need to generate correlated (positive as well as negative) bivariate exponential distribution with rate of 1/5 or any rate I need some guidance here. Please help. Regards, Sunny

Hi, I need help to simulate data to model pump's failure. Below is thought process to simulate the data: - For each Pump readings are captured via sensors each minute for period of 3 months. There are 10 pumps in total. - The failure rate or occurrences of event in 3 months for each Pump is defined. i.e Pump1 would fail 3 times in 3 months, Pump2 would fail 2 times in 3 months, etc. - The