Mark, Below is the sampled simulated granular data format for pumps for trial period of 3 months that I need to transform for survival analysis: 3 months = (60*24*90) minutes i.e 129600 minutes
pump_id timings events vibration temprature flow pump1 01-07-2017 00:00 0 3.443 69.6 139.806 pump1 01-07-2017 00:10 1 0.501 45.27 140.028 pump1 01-07-2017 00:20 0 2.031 52.9 137.698 pump1 01-07-2017 00:30 0 2.267 60.12 139.054 pump1 01-07-2017 00:40 1 2.267 60.12 139.054 pump1 01-07-2017 00:50 0 2.267 60.12 139.054 pump2 01-07-2017 00:00 0 3.443 69.6 139.806 pump2 01-07-2017 00:10 0 0.501 45.27 140.028 pump2 01-07-2017 00:20 0 2.031 52.9 137.698 pump2 01-07-2017 00:30 0 2.267 60.12 139.054 pump2 01-07-2017 00:40 1 2.267 60.12 139.054 pump2 01-07-2017 00:50 0 2.267 60.12 139.054 The above data set records observations and timings where 'pumps' experienced failure, tagged as '1' in column 'events'. In the above granular dataset the pump1 experiences 2 "event episodes." Below is the desired transformed format. the covariates in this data set will have the mean value: pump_id event_episodes event_status start(minutes) stop(minutes) pump1 1 1 0 10 pump1 2 1 10 40 pump1 3 0 40 129600 pump2 1 1 0 40 pump2 2 0 0 129600 ......... ......... The 'start' and 'stop' columns are evaluated from the 'timings' columns. I need help in performing such transformation in 'R'. Please guide and help. Regards, Sandeep On Wed, Jul 5, 2017 at 7:26 AM, Mark Sharp <msh...@txbiomed.org> wrote: > A small example data set that illustrates your question will be of great > value to those trying to help. This appears to be a transformation that you > are wanting to do (timestamp to units of time) so a data 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. > msh...@txbiomed.org > > > > > >> On Jul 4, 2017, at 7:02 AM, Sunny Singha <sunnysingha.analyt...@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. | 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 column of 'Timestamp' which is >> the column from transactional/granular data based on condition tagged in >> separate column, 'event 0/1, (i.e event ). >> >> Please guide how to do such transformation in 'R'. >> >> Regards, >> Sandeep >> >> >> >> On Wed, Jun 28, 2017 at 2:51 PM, Boris Steipe <boris.ste...@utoronto.ca> >> wrote: >> >>> 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, phase of the moon ... >>> - once you have a probability of failure, use the runif() function to >>> give you >>> a uniformly distributed random number in [0, 1]. If the number is >>> smaller than >>> your failure probability, accept the failure event, and record it. >>> - Repeat many times. >>> >>> Hope this helps. >>> B. >>> >>> >>> >>> >>>> On Jun 27, 2017, at 10:58 AM, sandeep Rana <sandyk...@gmail.com> wrote: >>>> >>>> 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 at 'Transactional level' or at >>> the granularity of time-minutes, where each observation is a reading >>> captured via Pump's sensors each minute. >>>> Once transactional data is prepared I Then need to summarise above data >>> for reliability/ survival analysis. >>>> >>>> To begin with below is the transactional data format that i want prepare: >>>> Pump-id| Timestamp | temp | vibration | suction pressure| discharge >>> pressure | Flow >>>> >>>> Above transactional data has to be prepared with below failure modes >>>> Defects : >>>> (1) Cavitation – very high in frequency but low impact >>>> (2) Bearing Damage – very low in frequency but high impact >>>> (3) Worn Shaft – medium frequency but medium impact >>>> >>>> I have used survsim package but that's not what I need here. >>>> Please help and guide. >>>> >>>> Regards, >>>> Sandeep >>>> >>>> ______________________________________________ >>>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >>>> https://stat.ethz.ch/mailman/listinfo/r-help >>>> PLEASE do read the posting guide http://www.R-project.org/ >>> posting-guide.html >>>> and provide commented, minimal, self-contained, reproducible code. >>> >>> ______________________________________________ >>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide http://www.R-project.org/ >>> posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >>> >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > > CONFIDENTIALITY NOTICE: This e-mail and any files and/or attachments > transmitted, may contain privileged and confidential information and is > intended solely for the exclusive use of the individual or entity to whom it > is addressed. 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