plot_sptime | R Documentation |
Make plots of the temporal trends for each region
fitted with pspatfit
function.
plot_sptime(object, data, time_var, reg_var)
object |
object returned from |
data |
either sf or dataframe with the data. |
time_var |
name of the temporal variable in data. |
reg_var |
name of the regional variable in data. |
time series plots of the temporal trend for each region
Roman Minguez | roman.minguez@uclm.es |
Roberto Basile | roberto.basile@univaq.it |
Maria Durban | mdurban@est-econ.uc3m.es |
Gonzalo Espana-Heredia | gehllanza@gmail.com |
Lee, D. and Durban, M. (2011). P-Spline ANOVA Type Interaction Models for Spatio-Temporal Smoothing. Statistical Modelling, (11), 49-69. <doi:10.1177/1471082X1001100104>
Eilers, P. and Marx, B. (2021). Practical Smoothing. The Joys of P-Splines. Cambridge University Press.
Fahrmeir, L.; Kneib, T.; Lang, S.; and Marx, B. (2013). Regression. Models, Methods and Applications. Springer.
Wood, S.N. (2017). Generalized Additive Models.
An Introduction with R
(second edition). CRC Press, Boca Raton.
library(pspatreg) data(unemp_it, package = "pspatreg") lwsp_it <- spdep::mat2listw(Wsp_it) ###### FORMULA OF THE MODEL form3d_psanova <- unrate ~ partrate + agri + cons + pspl(serv, nknots = 15) + pspl(empgrowth, nknots = 20) + pspt(long, lat, year, nknots = c(18, 18, 8), psanova = TRUE, nest_sp1 = c(1, 2, 3), nest_sp2 = c(1, 2, 3), nest_time = c(1, 2, 2)) ####### FIT the model sp3danova <- pspatfit(form3d_psanova, data = unemp_it, listw = lwsp_it, method = "Chebyshev") summary(sp3danova) ######## Plot of temporal trend for each province plot_sptime(sp3danova, data = unemp_it, time_var = "year", reg_var = "prov")
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