R help - Jan 2021 - Help with Using spTimer or spTDyn to estimate "GP" Model

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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On Sun, Jan 31, 2021 at 11:26 AM alex_rugu--- via R-help <
r-help at 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?
>
> ______________________________________________
> R-help at 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.
>
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