[R-sig-Geo] Help with spTimer or spTDyn to estimate the Bayesian Gaussian Process Model

[@iex_rugu m@iii@g oii y@hoo@com]

I am getting the following error when running the below script for the Bayesian Gaussian Process (GP) Model
using spTimer or spTDyn
#Error
 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. 

#######Script
library(spTimer)
library(spTDyn)
library(tidyverse)
library(ggmap)


register_google(key="your key")
getOption("ggmap")


#Data to be analyzed is from plm package
#It is a 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 rates 

#Get data
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)  #from ggmap

#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 framework


ts_STD <- def.time(t.series=ns, segments=ny)


##################Estimate the model using spTDyn package
#Note 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 how to deal with unbalanced panel
#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))))




Product_geo_unb <- Product_geo %>% filter(cond==1) %>% select(-cond)
glimpse(Product_geo_unb)


#How to use GibbsDyn or spT.Gibbs for such unbalanced panel data to estimate the Bayesian Gaussian Process (GP) Model?