plot_main_spt: Plot main terms in ANOVA spatial or spatio-temporal trends.

In rominsal/sptpsar: Spatio-Temporal Semiparametric Models with Spatial Lags

Plot main terms in ANOVA spatial or spatio-temporal trends.

Description

Plot main terms for spatial and temporal coordinates in spatial (2d) or spatio-temporal (3d) trends decomposed in ANOVA way.

Usage

plot_main_spt(spttrend, sp1, sp2, nT = 1, time = NULL,
  conflevel = 0.95)

Arguments

spttrend	object returned from fit_terms function including spttrend in the variables argument.
sp1	vector of first spatial coordinate.
sp2	vector of second spatial coordinate.
nT	Number of time periods (1 for non-temporal data). Default 1.
time	vector of temporal coordinate. It is NULL in spatial (2d) trends. Default NULL.
conflevel	numerical value for the confidence interval of the trend functions. Default 0.95.

Value

plot of each main trend (spatial and temporal) in ANOVA models.

Author(s)

Roman Minguez [e-mail]

References

• Lee, D. and Durbán, M. (2011). P-Spline ANOVA Type Interaction Models for Spatio-Temporal Smoothing. Statistical Modelling, (11), 49-69.

See Also

• fit_terms compute the terms for non-parametric trend and smooth functions for non-parametric continuous covariates .

• plot.gam plot the terms fitted by gam function in mgcv package.

Examples

################################################
 ###################### Examples using a panel data of rate of
 ###################### unemployment for 103 Italian provinces in period 1996-2014.
library(sptpsar)
data(unemp_it); Wsp <- Wsp_it
###############################################
 # Spatial (2d) semiparametric ANOVA model without spatial lag
 # Interaction term f12 with nested basis
form3 <- unrate ~ partrate + agri + cons +
                  pspl(serv,nknots=15) + pspl(empgrowth,nknots=20) +
                  pspt(long,lat,nknots=c(20,20),psanova=TRUE,
                  nest_sp1=c(1,2),nest_sp2=c(1,2))
# Spatial trend fixed for period 1996-2014
geospanova <- psar(form3,data=unemp_it)
summary(geospanova)
### Plot spatial trend (ANOVA)
spttrend <- fit_terms(geospanova,"spttrend")
lon <- scale(unemp_it$long); lat <- scale(unemp_it$lat)
### Plot main effects
plot_main_spt(spttrend,sp1=lon,sp2=lat,nT=19)

#'  ###############################################
 # Spatio-temporal (3d) semiparametric ANOVA model without spatial lag
 # Interaction terms f12,f1t,f2t and f12t with nested basis
 # Remark: It is necessary to include ntime as argument
 # Remark: nest_sp1, nest_sp2 and nest_time must be divisors of nknots
 form4 <- 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),ntime=19)
 sptanova <- psar(form4,data=unemp_it,
                  control=list(thr=1e-2,maxit=200,trace=FALSE))
 summary(sptanova)
 ### Plot spatial trend (ANOVA)
 spttrend <- fit_terms(sptanova,"spttrend")
 lon <- scale(unemp_it$long); lat <- scale(unemp_it$lat)
 time <- unemp_it$year
 ### Plot main effects
 plot_main_spt(spttrend,sp1=lon,sp2=lat,time=time,nT=19)

rominsal/sptpsar documentation built on June 1, 2022, 2:03 a.m.