README.md

In hunzikp/dimstar: Discrete Multivariate Spatio-temporal Autoregressive Models

dimstar: Discrete Multivariate Spatio-temporal Autoregressive Models

Philipp Hunziker July 13, 2017

dimstar is an R package implementing a novel MCEM algorithm for estimating latent-Guassian spatio-temporal autoregressive models for potentially multivariate binary and count outcomes.

Installation

Install dimstar straight from github:

require(devtools)
devtools::install_github('hunzikp/dimstar')

NOTE: This is a development version; documentation is pending. The package assumes that Python (2.7 / 3.6) and scipy are installed on your machine.

Usage

In the following, we replicate the county-level Spatial Probit analysis of the 1996 presidential elections reported in Section 5.1 of Liesenfeld et al. (2016). The outcome is a dummy indicating whether a county was won by Democratic candidate Bill Clinton.

## Dependencies
library(spdep)

## Loading required package: sp

## Loading required package: Matrix

library(Matrix)
library(dimstar)

## Prepare W
coords <- as.matrix(elections.df[,c("lon" ,"lat")])
el_knn <- knearneigh(coords, k = 6, longlat = TRUE)
el_nb <- knn2nb(knn = el_knn, sym = TRUE)
W <- nb2mat(el_nb, style = "W")
W <- Matrix(W, sparse = TRUE)

## Prepare X
X <- model.matrix(clinton ~ log_urban + prop_smcollege + prop_associate + prop_college + prop_gradprof, data = elections.df)

## Instantiate model object 
X.ls <- list(X)
y.ls <- list(elections.df$clinton)

model <- DISTAR$new(X.ls = X.ls, y.ls = y.ls, W_t = W, 
                         N = nrow(X), G = 1, TT = 1,
                         count = FALSE, 
                         spatial_dep = TRUE, 
                         temporal_dep = FALSE, 
                         outcome_dep = FALSE)

## [1] "Setting up log-determinants..."
## [1] "Done."

## Train
set.seed(0)
ch_vec <- model$train(maxiter = 15, M = 50, abs_tol = 1e-4, verbose = TRUE)

## [1] "Iteration 1; Max. parameter change = 6.172"
## [1] "Iteration 2; Max. parameter change = 0.773"
## [1] "Iteration 3; Max. parameter change = 0.738"
## [1] "Iteration 4; Max. parameter change = 0.233"
## [1] "Iteration 5; Max. parameter change = 0.147"
## [1] "Iteration 6; Max. parameter change = 0.611"
## [1] "Iteration 7; Max. parameter change = 0.628"
## [1] "Iteration 8; Max. parameter change = 0.199"
## [1] "Iteration 9; Max. parameter change = 0.966"
## [1] "Iteration 10; Max. parameter change = 0.289"
## [1] "Iteration 11; Max. parameter change = 0.083"
## [1] "Iteration 12; Max. parameter change = 0.339"
## [1] "Iteration 13; Max. parameter change = 0.137"
## [1] "Iteration 14; Max. parameter change = 0.641"
## [1] "Iteration 15; Max. parameter change = 0.045"

## Standard errors
model$compute_vcov(M = 100)

## Report
model$coeftest()

## 
## z test of coefficients:
## 
##                 Estimate Std. Error z value  Pr(>|z|)    
## (Intercept)     0.605057   0.120679  5.0138 5.338e-07 ***
## log_urban       4.275470   5.346903  0.7996  0.423933    
## prop_smcollege -2.838460   0.442548 -6.4139 1.418e-10 ***
## prop_associate  0.795818   0.845979  0.9407  0.346855    
## prop_college   -1.721855   0.808532 -2.1296  0.033204 *  
## prop_gradprof   4.474180   1.427493  3.1343  0.001723 ** 
## rho_1           0.643665   0.023906 26.9249 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Note that the estimates are near-identical to the ones reported by Liesenfeld et al. (2016).

hunzikp/dimstar documentation built on May 17, 2019, 6:36 a.m.