[R] Help with Using spTimer or spTDyn to estimate "GP" Model

[Bert Gunter]

Also note that you should have a higher likelihood of getting a helpful
response on the r-sig-geo list rather than here.

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, Jan 31, 2021 at 12:34 PM Bert Gunter <bgunter.4567 using gmail.com> wrote:

> Please not per the posting guide linked below:
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> 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, Jan 31, 2021 at 11:26 AM alex_rugu--- via R-help <
> r-help using r-project.org> wrote:
>
>> When I run the following scripts I get the following error and I do not
>> understand the source. I believe N is specified as the number of
>> observations by year in the time series variables.  The error is
>> #######################################
>> Output: GP models
>> Error in spGP.Gibbs(formula = formula, data = data, time.data =
>> time.data,  :
>>    Error: Years, Months, and Days are misspecified,
>>  i.e., total number of observations in the data set should be equal to N
>>   : N = n * r * T
>>    where, N = total number of observations in the data,
>>           n = total number of sites,
>>           r = total number of years,
>>           T = total number of days.
>> ## Check spT.time function.
>>
>> The scrip is
>> ######################
>> library(spTimer)
>> library(spTDyn)
>> library(tidyverse)
>> library(ggmap)
>>
>>
>> register_google(key=" your key ") # for use with ggmap
>> getOption("ggmap")
>>
>>
>> #Data to analyze is from plm package
>> #The data include US States Production, which is a panel of 48
>> observations from 1970 to 1986
>> #A data frame containing :
>> #state: the state
>> #year : the year
>> #region : the region
>> #pcap : public capital stock
>> #hwy :  highway and streets
>> #water : water and sewer facilities
>> #util : other public buildings and structures
>> #pc : private capital stock
>> #gsp : gross state product
>> #emp :labor input measured by the employment in non–agricultural payrolls
>> #unemp : state unemployment rateA panel of 48 observations from 1970 to
>> 1986
>>
>>
>> data("Produc", package = "plm")
>> glimpse(Produc)
>>
>>
>> #Estimate Geolocation of states to account for spill over effects
>> states_df <- data.frame(as.character(unique(Produc$state)))
>> names(states_df)<- c("state")
>>
>>
>> state_geo_df <- mutate_geocode(states_df, state)
>>
>> #Join the data
>>
>> Product_geo <- full_join(state_geo_df, Produc)
>> glimpse(Product_geo)
>>
>>
>> #Create the time series variable
>> #number of state
>> ns <- length(unique(Product_geo$state))
>>
>>
>> #number of year
>> ny <- length(unique(Product_geo$year))
>> ####################################################
>> # I want to do Spatio-Temporal Bayesian Modeling Using spTimer or spTDyn
>> #defines the time series in the Spatio-temporal data.
>>
>>
>> ts_STD <- def.time(t.series=ns, segments=ny)
>>
>>
>> ##################Estimate the model using spTDyn package
>> #Also note that the spT.Gibbs in spTimer gives the same error
>>
>>
>> GibbsDyn(gsp ~ pcap + hwy + water + util + pc ,
>>        data=Product_geo, model="GP",
>>          time.data=ts_STD,
>>          coords=~lon + lat,
>>           nItr=5000, nBurn=1000, report=1, tol.dist=0.05,
>>             distance.method="geodetic:km", cov.fnc="exponential",
>>
>>  spatial.decay=decay(distribution="FIXED"),truncation.para=list(at=0,lambda=2))
>>
>>
>> #Also I will appreciate showing how to deal with unbalanced panel data
>> #Delete some of the rows
>> Product_geo$cond = with(Product_geo,  if_else(state=="ALABAMA" &
>> year==1971, 0,
>>                                          if_else(state=="COLORADO" &
>> year==1971 | year==1973 , 0,
>>                                          if_else(state=="TEXAS" &
>> year==1971 | year==1973 | year==1985, 0, 1))))
>>
>> #Create an unbalanced panel
>> Product_geo_unb <- Product_geo %>% filter(cond==1) %>% select(-cond)
>> glimpse(Product_geo_unb)
>>
>>
>> #How to use GibbsDyn or spT.Gibbs function to estimate the "GP" model for
>> such unbalanced panel data?
>>
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>

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