plot_terms {pspatreg}

Plot terms of the non-parametric covariates in the semiparametric regression models.

Description

For each non-parametric covariate the plot of the term includes confidence intervals and the decomposition in fixed and random part when the term is reparameterized as a mixed model.

Usage

plot_terms(
  fitterms,
  data,
  type = "global",
  alpha = 0.05,
  listw = NULL,
  dynamic = FALSE,
  nt = NULL,
  decomposition = FALSE
)

Arguments

fitterms

object returned from fit_terms function.

data

dataframe or sf with the data.

type

type of term plotted between "global" (Default), "fixed" or "random".

alpha

numerical value for the significance level of the pointwise confidence intervals of the nonlinear terms. Default 0.05.

listw

used to compute spatial lags for Durbin specifications. Default = 'NULL'

dynamic

Logical value to set a dynamic model. Dynamic models include a temporal lag of the dependent variable in the right-hand side of the equation. Default = 'FALSE'.

nt

Number of temporal periods. It is needed for dynamic models.

decomposition

Plot the decomposition of term in random and fixed effects.

Value

list with the plots of the terms for each non-parametric covariate included in the object returned from fit_terms.

Author(s)

Roman Minguez	[e-mail]
Roberto Basile	[e-mail]
Maria Durban	[e-mail]
Gonzalo Espana-Heredia	[e-mail]

References

• Wood, S.N. (2017). Generalized Additive Models. An Introduction with R (second edition). CRC Press, Boca Raton.

See Also

• fit_terms compute smooth functions for non-parametric continuous covariates.

• impactsnopar plot the effects functions of non-parametric covariates.

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

Examples

################################################
# Examples using spatial data of Ames Houses.
###############################################
# Getting and preparing the data
library(pspatreg)
library(spdep)
library(sf)
ames <- AmesHousing::make_ames() # Raw Ames Housing Data
ames_sf <- st_as_sf(ames, coords = c("Longitude", "Latitude"))
ames_sf$Longitude <- ames$Longitude
ames_sf$Latitude <- ames$Latitude
ames_sf$lnSale_Price <- log(ames_sf$Sale_Price)
ames_sf$lnLot_Area <- log(ames_sf$Lot_Area)
ames_sf$lnTotal_Bsmt_SF <- log(ames_sf$Total_Bsmt_SF+1)
ames_sf$lnGr_Liv_Area <- log(ames_sf$Gr_Liv_Area)
ames_sf1 <- ames_sf[(duplicated(ames_sf$Longitude) == FALSE), ]
                    
form1 <- lnSale_Price ~ Fireplaces + Garage_Cars +
          pspl(lnLot_Area, nknots = 20) + 
          pspl(lnTotal_Bsmt_SF, nknots = 20) +
          pspl(lnGr_Liv_Area, nknots = 20)    

 
########### Constructing the spatial weights matrix
coord_sf1 <- cbind(ames_sf1$Longitude, ames_sf1$Latitude)
k5nb <- knn2nb(knearneigh(coord_sf1, k = 5, 
                          longlat = TRUE, use_kd_tree = FALSE), sym = TRUE)
lw_ames <- nb2listw(k5nb, style = "W", 
                  zero.policy = FALSE)
gamsar <- pspatfit(form1, data = ames_sf1, 
                   type = "sar", listw = lw_ames,
                   method = "Chebyshev")
summary(gamsar)
list_varnopar <- c("lnLot_Area", "lnTotal_Bsmt_SF", 
"lnGr_Liv_Area")
terms_nopar <- fit_terms(gamsar, list_varnopar)
######################  Plot non-parametric terms
plot_terms(terms_nopar, ames_sf1)

###### Examples using a panel data of rate of
###### unemployment for 103 Italian provinces in period 1996-2014.
library(pspatreg)
data(unemp_it, package = "pspatreg")
lwsp_it <- spdep::mat2listw(Wsp_it)

########  No Spatial Trend: ps-sar including a spatial 
########  lag of the dependent variable
form1 <- unrate ~ partrate + agri + cons + 
                  pspl(serv,nknots = 15) +
                  pspl(empgrowth,nknots = 20) 
gamsar <- pspatfit(form1, data = unemp_it, 
                   type = "sar", listw = Wsp_it)
summary(gamsar)
########  Fit non-parametric terms (spatial trend must be name "spttrend")
list_varnopar <- c("serv", "empgrowth")
terms_nopar <- fit_terms(gamsar, list_varnopar)
#######  Plot non-parametric terms
plot_terms(terms_nopar, unemp_it)