SEMCMC: Function to fit spatial econometrics models using MCMC with...

In becarioprecario/SEMCMC: Bayesian Spatial Econometrics Models with MCMC using Jags and Stan

Description

This function will fit several spatial econometrics models with jags. Models included are SEM, SLM, SDM, SDEM, SLX, SAC and SMA.

Usage

SEMCMC(
  formula,
  data,
  W,
  model = "sem",
  link = "identity",
  n.burnin = 1000,
  n.iter = 1000,
  n.thin = 1,
  linear.predictor = FALSE,
  sampler = "jags",
  INLA = FALSE
)

Arguments

formula	Formula with response and covariates.
data	Data.frame with the dataset.
W	An adjacency matrix, same as used in the call to SEMCMC().
model	Model to be fitted: 'sem', 'slm', 'sdm', 'sdem', 'slx', 'sac', 'sacmixed' (SAC with lagged covariates), 'sma', 'smamixed' or 'car'.
link	One of 'indentity', 'logit' or 'probit'.
n.burnin	Number of burn-in iterations
n.iter	Number of iterarions after bun-in
n.thin	Thinning interval
linear.predictor	Whether the linear predictor should be saved (default is FALSE).
sampler	One of 'jags' (default) or 'stan'.
INLA	A boolean variable to decide whether the hierarchical model is specified as with R-INLA. This is an experimental feature mainly for comparisson purposes and only implemented for the SEM model (in jags).

Value

A named list with MCMC objects as returned by jags.

See Also

lagsarlm, errorsarlm and sacsarlm to fit similar models using maximum likelihood.

Examples

data(columbus, package = "spdep")

W <- spdep::nb2mat(col.gal.nb, style = "W")
m.form <-  CRIME ~ INC + HOVAL

#Fit models with SEMCMC
sem.jags <- SEMCMC(m.form, data = columbus, W = W, model = "sem", sampler = "jags")
sem.stan <- SEMCMC(m.form, data = columbus, W = W, model = "sem", sampler = "stan")

 #Compute impacts
impacts(sem.jags, W)
impacts(sem.stan, W)
## Not run: 
slm.jags <- SEMCMC(m.form, data = columbus, W = W, model = "slm", sampler = "jags")
slm.stan <- SEMCMC(m.form, data = columbus, W = W, model = "slm", sampler = "stan")
sdm.jags <- SEMCMC(m.form, data = columbus, W = W, model = "sdm", sampler = "jags")
sdm.stan<- SEMCMC(m.form, data = columbus, W = W, model = "sdm", sampler = "stan")
sdem.jags <- SEMCMC(m.form, data = columbus, W = W, model = "sdem", sampler = "jags")
sdem.stan <- SEMCMC(m.form, data = columbus, W = W, model = "sdem", sampler = "stan")
slx.jags <- SEMCMC(m.form, data = columbus, W = W, model = "slx",  sampler = "jags")
slx.stan <- SEMCMC(m.form, data = columbus, W = W, model = "slx", sampler = "stan")
sac.jags <- SEMCMC(m.form, data = columbus, W = W, model = "sac", sampler = "jags")
sac.stan <- SEMCMC(m.form, data = columbus, W = W, model = "sac", sampler = "stan")
sacmixed.jags <- SEMCMC(m.form, data = columbus, W = W, model = "sacmixed", sampler = "jags")
sacmixed.stan <- SEMCMC(m.form, data = columbus, W = W, model = "sacmixed", sampler = "stan")

#Use binary adjancecy matrix with CAR models
W.bin <- spdep::nb2mat(col.gal.nb, style = "B")

car.jags <- SEMCMC(m.form, data = columbus, W = W.bin, model = "car",  sampler = "jags")
car.stan <- SEMCMC(m.form, data = columbus, W = W.bin, model = "car", sampler = "stan")

#SMA model requires 'W' (reponse term) and 'W.bin' (error term)
sma.jags <- SEMCMC(m.form, data = columbus, W = list(W, W.bin), model = "sma", sampler = "jags")
smamixed.jags <- SEMCMC(m.form, data = columbus, W = list(W, W.bin), model = "smamixed", sampler = "jags")
#Compute impacts
impacts(slm.jags, W)
impacts(slm.stan, W)
impacts(sdm.jags, W)
impacts(sdm.stan, W)
impacts(sdem.jags, W)
impacts(sdem.stan, W)
impacts(slx.jags, W)
impacts(slx.stan, W)
impacts(sac.jags, W)
impacts(sma.jags, W)
impacts(smamixed.jags, W)
impacts(sac.stan, W)
impacts(sacmixed.jags, W)
impacts(sacmixed.stan, W)
impacts(car.jags, W)
impacts(car.stan, W)

## End(Not run)

#Example on logit and probit models
## Not run: 
#Example form the spatialprobit package using the Katrina dataset
 data(Katrina, package = "spatialprobit")
#Subset 100 shops
set.seed(1)
Katrina.red <- Katrina[sample(1:nrow(Katrina), 50), ]
 nb <- spdep::knn2nb(spdep::knearneigh(cbind(Katrina.red$lat, Katrina.red$long), k=11))
 W <- spdep::nb2mat(nb, style="W")

m.formlogit <- y1 ~ flood_depth + log_medinc + small_size + large_size +
  low_status_customers +  high_status_customers + owntype_sole_proprietor +
  owntype_national_chain
 #Logit model
 semlogit.jags <- SEMCMC(m.formlogit, data = Katrina.red, W = W, 
   model = "sem", sampler = "jags", link = "logit")
 semlogit.stan <- SEMCMC(m.formlogit, data = Katrina.red, W = W,
   model = "sem", sampler = "stan", link = "logit")
 #Probit model
 semprobit.jags <- SEMCMC(m.formlogit, data = Katrina.red, W = W,
   model = "sem", sampler = "jags", link = "probit")
 semprobit.stan <- SEMCMC(m.formlogit, data = Katrina.red, W = W,
   model = "sem", sampler = "stan", link = "probit")

## End(Not run)

becarioprecario/SEMCMC documentation built on Feb. 25, 2021, 4:03 p.m.