R package: Spatio-Temporal Dynamic Generalized Linear Models (STDGLM) by Carlo Zaccardi
The STDGLM package provides a framework for fitting spatio-temporal dynamic generalized linear models. These models are useful for analyzing data that varies over both space and time, allowing for the incorporation of spatial and temporal dependencies in the modeling process. The package provides functions for fitting these models, as well as tools for visualizing and interpreting the results.
This package has been developed with funding from High-resolution Data Fusion for AIR Quality MAPping (HAIRQ-MAP), a research project led by a team at the University “G. d’Annunzio” of Chieti-Pescara (UdA).
Funding acknowledgement
Financial support from the National Recovery and Resilience Plan GRINS - PE0000018 - BAC “High-Resolution Data Fusion for Air Quality Mapping - HAIRQ-MAP”, SPOKE 0 e 2 - CUP J33C22002910001.
Installation
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
require(devtools)
}
devtools::install_github("czaccard/STDGLM")Quick Usage Example
data(ApuliaAQ)
p = length(unique(ApuliaAQ$AirQualityStation)) # 51
t = length(unique(ApuliaAQ$time)) # 365
# distance matrix
W = as.matrix(dist(cbind(ApuliaAQ$Longitude[1:p], ApuliaAQ$Latitude[1:p])))
# response variable: temperature
y = matrix(ApuliaAQ$CL_t2m, p, t)
# covariates (intercept + altitude)
X = array(1, dim = c(p, t, 2))
X[,,2] = matrix(ApuliaAQ$Altitude, p, t)
mod <- stdglm(y=y, X=X, W=W)How to Cite / Acknowledge
If you use the STDGLM package in published research, please run the following R code to generate a citation:
citation("STDGLM")We acknowledge the funding:
HAIRQ-MAP — High-resolution Data Fusion for AIR Quality MAPping. University “G. d’Annunzio” of Chieti-Pescara. Funded by the National Recovery and Resilience Plan GRINS - PE0000018 - BAC “High-Resolution Data Fusion for Air Quality Mapping - HAIRQ-MAP”, SPOKE 0 e 2 - CUP J33C22002910001.
