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This package provides a framework for specifying spatially, temporally and spatially-and-temporally varying coefficient models using Generalized Additive Models (GAMs) with smooths. It builds on GAM functionality from themgcv package. The smooths are parameterised with location, time and predictor variables. The framework supports the investigation of the presence and nature of any space-time dependencies in the data by evaluating multiple model forms (specifications) using a Generalized Cross-Validation (GCV) score. The workflow sequence is to i) Prepare the data (data.frame,tibble orsf object) by lengthening it to have a single location and time variables for each observation. ii) Evaluate all possible spatial and/or temporal models in which each predictor is specified in different ways. iii) Evaluate the models via their GCV score and to pick the best model (the one with the lowest GCV). iv) Create the final model. v) Calculate the varying coefficient estimates to quantify how the relationships between the target and predictor variables vary over space, time or space-time. vi) Create maps, time series plots etc. For more details see: Comber et al (2023) [https://doi.org/10.4230/LIPIcs.GIScience.2023.22], Comber et al (2024) [https://doi.org/10.1080/13658816.2023.2270285] and Comber et al (2004) [https://doi.org/10.3390/ijgi13120459].

You can install the CRAN version of stgam :

install.packages("stgam")

Or the development version:

# just the packageremotes::install_github("lexcomber/stgam")# with the vignettes - takes a bit longerremotes::install_github("lexcomber/stgam",build_vignettes=TRUE,force=T)

This code below loads the package and undertakes the proposed workflow for a spatially varying coefficient model using GAMs with spatial smooths:

# a spatially varying coefficient model examplelibrary(stgam)library(dplyr)library(ggplot2)library(cols4all)# define input datadata("hp_data")input_data<-hp_data|># create Intercept as an addressable term  mutate(Intercept=1)# evaluate different model formssvc_mods<-  evaluate_models(input_data=input_data,target_var="priceper",vars= c("pef","beds"),coords_x="X",coords_y="Y",VC_type="SVC",time_var=NULL,ncores=2  )# rank the modelsmod_comp<- gam_model_rank(svc_mods)# have a lookmod_comp|> select(-f)# select best modelf= as.formula(mod_comp$f[1])# put into a `mgcv` GAM modelgam.m= gam(f,data=input_data)# calculate the Varying Coefficientsterms= c("Intercept","pef")vcs= calculate_vcs(input_data,gam.m,terms)vcs|> select(priceper,yot,X,Y, starts_with(c("b_","se_")),yhat)# map themdata(lb)tit<-expression(paste(""*beta[`pef`]*"")) ggplot()+   geom_sf(data=lb,col="lightgrey")+  geom_point(data=vcs, aes(x=X,y=Y,col=b_pef))+   scale_colour_continuous_c4a_div("brewer.rd_yl_bu",name=tit)+  theme_bw()+  coord_sf()+  xlab("")+ ylab("")