There are several CRAN Task Views. Some of them should intersect with your question. I don’t think your description of the problem suggest that multivariate correlation is the best approach. Some sort of optimization or numerical simulation would seem to be more fruitful.
— David Sent from my iPhone > On May 22, 2022, at 12:01 PM, Bernard Comcast <mcgarvey.bern...@comcast.net> > wrote: > > Its simply a query to know what tools/packages R has for correlating single > values with multivalued vectors. If that is outside the scope of the PG then > so be it. > > Bernard > > Sent from my iPhone so please excuse the spelling!" > >> On May 22, 2022, at 1:52 PM, Bert Gunter <bgunter.4...@gmail.com> wrote: >> >> >> Please read the posting guide(PG) inked below. Your query sounds more like a >> project that requires a paid consultant; if so, this is way beyond the scope >> of this list as described in the PG. So don't be too surprised if you don't >> get a useful response, which this isn't either of course. >> >> >> Bert Gunter >> >> "The trouble with having an open mind is that people keep coming along and >> sticking things into it." >> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) >> >> >>>> On Sun, May 22, 2022 at 10:40 AM Bernard McGarvey >>>> <mcgarvey.bern...@comcast.net> wrote: >>> I work in aspects of Cold Chain transportation in the pharmaceutical >>> industry. These shippers are used to transport temperature sensitive >>> products by surrounding the product load box with insulating materials of >>> various sorts. The product temperature has lower and upper allowed limits >>> so that when the product temperature hits one of these limits, the shipper >>> fails and this failure time is teh shipper duration. If the shipper is >>> exposed to very low or very high ambient temperatures during a shipment >>> then we expect the duration of the shipper to be low. >>> >>> The particular problem I am currently undertaking is to create a fast way >>> to predict the duration of a shipping container when it is exposed to a >>> given ambient temperature. >>> >>> Currently we have the ability to predict such durations using a calibrated >>> 3D model (typically a finite element or finite volume transient >>> representation of the heat transfer equations). These models can predict >>> the temperature of the pharmaceutical product within the shipper over time >>> as it is exposed to an external ambient temperature profile. . >>> >>> The problem with the 3D model is that it takes significant CPU time and the >>> software is specialized. What I would like to do is to be able to enter the >>> ambient profile into a spreadsheet and then be able to predict the expected >>> duration of the shipper using a simple calculation that can be implemented >>> in the spreadsheet environment. The idea I had was as follows: >>> >>> 1. Create a selection of ambient temperature profiles covering a wide range >>> of ambient behavior. Ensure the profiles are long enough so that the >>> shipper is sure to fail at some time during the ambient profile. >>> >>> 2. Use the 3D model to predict the shipper duration for the selection of >>> ambient temperature profiles in (1). Each ambient temperature will have its >>> own duration. >>> >>> 3. Since only the ambient temperatures up to the duration time are >>> relevant, truncate each ambient profile for times greater than the duration. >>> >>> 4. Step (3) means that the ambient temperature profiles will have different >>> lengths corresponding to the different durations. >>> >>> 5. Use the truncated ambient profiles and their corresponding durations to >>> build some type of empirical model relating the duration to the >>> corresponding ambient profile. >>> >>> Some other notes: >>> >>> a. We know from our understanding of how the shippers are constructed and >>> the laws of heat transfer that some sections of the ambient profile will >>> have more of an impact on determining the duration that other sections. >>> b. Just correlating the duration with the average temperature of the >>> profile can predict the duration for that profile to within 10-15%. We are >>> looking for the ability to get within 2% of the shipper duration predicted >>> by the 3D model. >>> >>> What I am looking for is suggestions as to how to approach step (5) with >>> tools/packages available in R. >>> >>> Thanks in advance >>> >>> Bernard McGarvey, Ph.D. >>> >>> Technical Advisor >>> Parenteral Supply Chain LLC >>> >>> bernard.first.princip...@gmail.com mailto:bernard.first.princip...@gmail.com >>> >>> (317) 627-4025 >>> >>> >>> >>> [[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. > > [[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. ______________________________________________ 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.
