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 <[email protected]> wrote: > Please not per the posting guide linked below: > > "For questions about functions in standard packages distributed with R > (see the FAQ Add-on packages in R > <https://cran.r-project.org/doc/FAQ/R-FAQ.html#Add-on-packages-in-R>), > ask questions on R-help. > If the question relates to a *contributed package* , e.g., one downloaded > from CRAN, try contacting the package maintainer first. You can also use > find("functionname") and packageDescription("packagename") to find this > information. *Only* send such questions to R-help or R-devel if you get > no reply or need further assistance. This applies to both requests for help > and to bug reports." > > So do not be disappointed if you do not receive a (helpful) response here. > You could, but ... There are, after all, around 25,000 R packages out > there, and this list cannot possibly support them all. > > 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 < > [email protected]> 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? >> >> ______________________________________________ >> [email protected] 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]] ______________________________________________ [email protected] 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.
