[R] Time series (trend over time) for irregular sampling dates and multiple sites

[Bert Gunter]

I have 0 expertise, but I suggest that you check out the SPatioTemporal
taskview on CRAN (or possibly others, like environmetrics). You might also
want to move this to the R-Sig-geo list,where you probably are more likely
to find relevant expertise.

Cheers,
Bert

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 Tue, Apr 30, 2019 at 8:13 AM Catarina Serra Gonçalves <
catarinasg using gmail.com> wrote:

> I have a dataset of marine debris items (number of items standardized per
> effort: Items/(number of volunteers*Hours*Lenght)) taken from 2 main
> locations (WA and Queensland) in Australia (8 Sub Sites in total: 4 in WA
> and 4 in Queensland) at irregular sampling intervals over a period 15
> years.
>
> I want to test if there is a change over the years on the amount of debris
> in these locations and more specifically a change after the implementation
> of a mitigation strategy (in 2013).
> Here’s the head of the data:[image: enter image description here]
> <https://i.stack.imgur.com/VNIpb.png>Description of each one of the
> varables in the dataframe:
>
> *eventid *= each sampling (clean-up) event Location = Queensland and New
> South Wales Sites = all the 9 sampling beaches
>
> *Date *= specific dates for the clean-up events (day-month-year)
>
> *Date1 *= specific dates for the clean-up events (day-month-year) on the
> POSICXT format Year= Year of sampling event (2004 to 2018)
>
> *Month*= Month of the sampling event (jan to dec)
>
> *nMonth*= a number was determined to the respective month of the sampling
> event (1 to 12)
>
> *Day*= Day of sampling (1 to 31) Days = Days since the first date of clean
> up = just another way of using the dates
>
> *MARPOL *= before and after implementation (factor with 2 levels)
>
> *DaysC *= days between sampling events for the same sites = number of days
> since the previous clean-up event
>
> *DaysI *= Days since intervention, all the dates before implementation are
> zero, and after we count the number of days since the implementation date
> (1 jan 2013)
>
> *DaysIa*= same as DayI but instead of zero for before the intervention we
> have negative values (days)
>
> *Items *= number of fishing and shipping items counted in each clean-up
> event
>
> *Hours *= hours spent by all volunteers together at each clean up event
>
> *Lenght *= Lenght of beach sampled by all volunteers together at each clean
> up event volunteers = all volunteers at each clean up event
>
> *HoursVolunteer *= hours spent bt each volunteer at each clean up event
> (Hours/volunteers)
>
> *Ieffort *= the items standarized by the effort (hours, volunteers and
> lenght)
>
> *GrossWeight & **GrossTotal are not relevant *
> ------------------------------
> Problems:
>
> My data has a few problems: (1) I think I will need to fix the effects of
> seasonal variation (Monthly) and (2) of possible spatial correlation
> (probability of finding an item is higher after finding one since they can
> come from the same ship). (3) How do I handle the fact that the
> measurements were not taken at a regular interval?
>
> I was trying to use GAMs to analyse the data and see the trends over time.
> The model I came across is the following:
>
> m4<- gamm(Ieffort ~ s(DaysIa)+MARPOL+ s(nMonth, bs = "ps", k = 12),
> random=list(Site=~1,Location=~1),data = d)
>
> *thank you in advance.*
> -
> *Catarina Serra Gonçalves *
> PhD candidate
>
> Adrift Lab  <https://adriftlab.org>
> University of Tasmania <http://www.utas.edu.au/> | Institute for Marine
> and
> Antarctic Studies  <http://www.imas.utas.edu.au/>
> Launceston, TAS | Australia
>
> Personal website <https://catarinasg.wixsite.com/acserra>
> <https://catarinasg.wixsite.com/acserra>| E-mail  <acserra using utas.edu.au> |
> Twitter <https://twitter.com/CatarinaSerraG>
> Research Gate
> <https://www.researchgate.net/profile/Catarina_Serra_Goncalves> | Google
> Scholar <https://scholar.google.pt/citations?user=8nBrRFwAAAAJ&hl=en>
>
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>
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