Nothing
R/plot_impactsnopar.R
In pspatreg: Spatial and Spatio-Temporal Semiparametric Regression Models with Spatial Lags
Defines functions
plot_impactsnopar
Documented in
plot_impactsnopar
#' @name plot_impactsnopar
#' @rdname plot_impactsnopar
#'
#' @title Plot direct, indirect and total impacts functions
#' for continous non-parametric covariates in semiparametric spatial regression models.
#'
#' @description Plot direct, indirect and total impacts functions for
#' non-parametric covariates included in a semiparametric spatial
#' or spatio-temporal SAR model. This model must include a spatial
#' lag of the dependent variable (SAR) to have indirect effects
#' different from 0, otherwise, total and direct function effects
#' are the same. The effect functions can be smoothed to overcome
#' the instabilities created by the premultiplication of matrix
#' \eqn{(I - \rho W)^{-1}}
#'
#' @param impactsnopar object returned from \code{\link{impactsnopar}} function.
#' @param data dataframe with the data.
#' @param smooth logical value to choose smoothing of the effects function
#' prior to plot. Default TRUE.
#' @param span span for the kernel of the smoothing (see \code{\link{loess}}
#' for details). Default c(0.1, 0.1, 0.2).
#' @param 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`.
#' @param nt Number of temporal periods. It is needed
#' for dynamic models.
#'
#' @return plot of the direct, indirect and total impacts function for each non-parametric
#' covariate included in the object returned from \code{\link{impactsnopar}}.
#'
#' @author
#' \tabular{ll}{
#' Roman Minguez \tab \email{roman.minguez@@uclm.es} \cr
#' Roberto Basile \tab \email{roberto.basile@@univaq.it} \cr
#' Maria Durban \tab \email{mdurban@@est-econ.uc3m.es} \cr
#' Gonzalo Espana-Heredia \tab \email{gehllanza@@gmail.com} \cr
#' }
#'
#' @seealso
#' \itemize{
#' \item \code{\link{impactsnopar}} compute total, direct and indirect effect
#' functions for non-parametric continuous covariates.
#' \item \code{\link{fit_terms}} compute smooth functions for non-parametric
#' continuous covariates.
#' \item \code{\link{plot_terms}} plot the terms of non-parametric covariates.
#' }
#'
#' @references \itemize{
#' \item Basile, R.; Durban, M.; Minguez, R.; Montero, J. M.; and
#' Mur, J. (2014). Modeling regional economic dynamics: Spatial
#' dependence, spatial heterogeneity and nonlinearities.
#' \emph{Journal of Economic Dynamics and Control}, (48), 229-245.
#' <doi:10.1016/j.jedc.2014.06.011>
#' }
#'
#' @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$Longitude) == FALSE), ]
#'
#' form1 <- lnSale_Price ~ Fireplaces + Garage_Cars +
#' pspl(lnLot_Area, nknots = 20) +
#' pspl(lnTotal_Bsmt_SF, nknots = 20) +
#' pspl(lnGr_Liv_Area, nknots = 20)
#'
#' \donttest{
#' ########### Constructing the spatial weights matrix
#' coord_sf1 <- cbind(ames_sf1$Longitude, ames_sf1$Latitude)
#' k5nb <- knn2nb(knearneigh(coord_sf1, k = 5,
#' longlat = TRUE, use_kd_tree = FALSE), sym = TRUE)
#' lw_ames <- nb2listw(k5nb, style = "W",
#' zero.policy = FALSE)
#' gamsar <- pspatfit(form1, data = ames_sf1,
#' type = "sar", listw = lw_ames,
#' method = "Chebyshev")
#' summary(gamsar)
#' nparimpacts <- impactsnopar(gamsar, listw = lw_ames, viewplot = FALSE)
#' plot_impactsnopar(nparimpacts, data = ames_sf1, smooth = TRUE)
#' ###### Examples using a panel data of rate of
#' ###### unemployment for 103 Italian provinces in period 1996-2014.
#' library(pspatreg)
#' data(unemp_it)
#' ## Wsp_it is a matrix. Create a neighboord list
#' lwsp_it <- spdep::mat2listw(Wsp_it)
#' ## short sample for spatial pure case (2d)
#' ######## No Spatial Trend: PSAR including a spatial
#' ######## lag of the dependent variable
#' form1 <- unrate ~ partrate + agri + cons + empgrowth +
#' pspl(serv, nknots = 15)
#' gamsar <- pspatfit(form1, data = unemp_it,
#' type = "sar",
#' listw = lwsp_it)
#' summary(gamsar)
#' ###### Non-Parametric Total, Direct and Indirect impacts
#' imp_nparvar <- impactsnopar(gamsar, alpha = 0.05,
#' listw = lwsp_it,
#' viewplot = TRUE)
#' ##### This returns the same result but using plot_impactsnopar()
#' imp_nparvar <- impactsnopar(gamsar, listw = lwsp_it, alpha = 0.05,
#' viewplot = FALSE)
#' plot_impactsnopar(imp_nparvar, data = unemp_it,
#' smooth = TRUE)
#' }
#'
#' @export
plot_impactsnopar <- function(impactsnopar, data, smooth = TRUE,
span = c(0.1, 0.1, 0.2),
dynamic = FALSE,
nt = NULL) {
if (inherits(data, "sf"))
data <- st_drop_geometry(data)
nfull <- nrow(data)
if (dynamic) {
if (is.null(nt))
stop("plot_impactsnopar function needs nt as argument for dynamic models")
idxyear1 <- seq(from = 1, to = nfull, by = nt)
data <- data[-idxyear1, ]
}
tot <- impactsnopar$impnopar_tot
if (nrow(tot) != nrow(data))
stop("Dimensions of impacts and data disagree.
Likely the model is dynamic and arguments dynamic = TRUE and nt value
are needed in plot_impactsnopar")
uptot <- impactsnopar$impnopar_tot_up
lowtot <- impactsnopar$impnopar_tot_low
dir <- impactsnopar$impnopar_dir
updir <- impactsnopar$impnopar_dir_up
lowdir <- impactsnopar$impnopar_dir_low
ind <- impactsnopar$impnopar_ind
upind <- impactsnopar$impnopar_ind_up
lowind <- impactsnopar$impnopar_ind_low
for (i in 1:ncol(tot)) {
name_var <- colnames(tot)[i]
var <- as.matrix(data[, c(name_var)])
colnames(var) <- name_var
ord <- order(var)
tot_i <- matrix(tot[, c(name_var)], ncol = 1)
colnames(tot_i) <- name_var
uptot_i <- matrix(uptot[, c(name_var)], ncol = 1)
colnames(uptot_i) <- name_var
lowtot_i <- matrix(lowtot[, c(name_var)], ncol = 1)
colnames(lowtot_i) <- name_var
dir_i <- matrix(dir[, c(name_var)], ncol = 1)
colnames(dir_i) <- name_var
updir_i <- matrix(updir[,c(name_var)], ncol = 1)
colnames(updir_i) <- name_var
lowdir_i <- matrix(lowdir[,c(name_var)], ncol = 1)
colnames(lowdir_i) <- name_var
ind_i <- matrix(ind[,c(name_var)], ncol = 1)
colnames(ind_i) <- name_var
upind_i <- matrix(upind[,c(name_var)], ncol = 1)
colnames(upind_i) <- name_var
lowind_i <- matrix(lowind[,c(name_var)], ncol = 1)
colnames(lowind_i) <- name_var
if (smooth) {
span_tot <- span[1]
span_dir <- span[2]
span_ind <- span[3]
tot_i_smooth <- predict(loess(tot_i ~ var, span = span_tot),
method = "loess()")
uptot_i_smooth <- predict(loess(uptot_i ~ var, span = span_tot),
method = "loess()")
lowtot_i_smooth <- predict(loess(lowtot_i ~ var, span = span_tot),
method = "loess()")
if (sum(is.nan(tot_i_smooth)) > 1 ||
sum(is.nan(uptot_i_smooth)) > 1 ||
sum(is.nan(lowtot_i_smooth)) > 1 ) {
warning(paste("Smoothing of total impacts with variable ", name_var,
" produces NaN. ",
"This variable is not suited for smoothing \n", sep = ""))
} else {
tot_i <- tot_i_smooth
uptot_i <- uptot_i_smooth
lowtot_i <- lowtot_i_smooth
}
dir_i_smooth <- predict(loess(dir_i ~ var, span = span_dir),
method = "loess()")
updir_i_smooth <- predict(loess(updir_i ~ var, span = span_dir),
method = "loess()")
lowdir_i_smooth <- predict(loess(lowdir_i ~ var, span = span_dir),
method = "loess()")
if (sum(is.nan(dir_i_smooth)) > 1 ||
sum(is.nan(updir_i_smooth)) > 1 ||
sum(is.nan(lowdir_i_smooth)) > 1 ) {
warning(paste("Smoothing of direct impacts with variable ", name_var,
" produces NaN. ",
"This variable is not suited for smoothing \n", sep = ""))
} else {
dir_i <- dir_i_smooth
updir_i <- updir_i_smooth
lowdir_i <- lowdir_i_smooth
}
ind_i_smooth <- predict(loess(ind_i ~ var, span = span_ind),
method = "loess()")
upind_i_smooth <- predict(loess(upind_i~var, span = span_ind),
method = "loess()")
lowind_i_smooth <- predict(loess(lowind_i ~ var, span = span_ind),
method = "loess()")
if (sum(is.nan(ind_i_smooth)) > 1 ||
sum(is.nan(upind_i_smooth)) > 1 ||
sum(is.nan(lowind_i_smooth)) > 1 ) {
warning(paste("Smoothing of indirect impacts with variable ", name_var,
" produces NaN. ",
"This variable is not suited for smoothing \n", sep = ""))
} else {
ind_i <- ind_i_smooth
upind_i <- upind_i_smooth
lowind_i <- lowind_i_smooth
}
}
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar))
par(mfrow = c(1, 1))
plot(var[ord], tot_i[ord],
type = "l",
ylab = paste("f(", name_var, ")"),
xlab = name_var,
ylim = c(min(lowtot_i), max(uptot_i)),
cex.lab = 1.0,
col = 2,
lty = 1,
lwd = 2,
cex.main = 1.0,
main = paste("Total Impacts: ", name_var))
lines(var[ord], uptot_i[ord],
xlab = "", ylab = "",
type = "l", col = 2, lty = 2, lwd = 1.5)
lines(var[ord],lowtot_i[ord],
xlab = "",
ylab = "",
type = "l",
col = 2,
lty = 2,
lwd = 1.5)
abline(a = 0, b = 0)
readline(prompt="Press [enter] to continue")
plot(var[ord], dir_i[ord],
type = "l",
ylab = paste("f(", name_var, ")"),
xlab = name_var,
ylim = c(min(lowdir_i), max(updir_i)),
cex.lab = 1.0,
col = 3,
lty = 1,
lwd = 2,
cex.main = 1.0,
main = paste("Direct Impacts:", name_var))
lines(var[ord], updir_i[ord],
xlab = "",
ylab = "",
type = "l",
col = 3,
lty = 2,
lwd = 1.5)
lines(var[ord],lowdir_i[ord],
xlab = "",
ylab = "",
type = "l",
col = 3,
lty = 2,
lwd = 1.5)
abline(a = 0, b = 0)
readline(prompt="Press [enter] to continue")
plot(var[ord], ind_i[ord],
type = "l",
ylab = paste("f(", name_var, ")"),
xlab = name_var,
ylim = c(min(lowind_i), max(upind_i)),
cex.lab = 1.0,
col = 4,
lty = 1,
lwd = 2,
cex.main = 1.0,
main = paste("Indirect Impacts: ", name_var))
lines(var[ord], upind_i[ord],
xlab = "",
ylab = "",
type = "l",
col = 4,
lty = 2,
lwd = 1.5)
lines(var[ord], lowind_i[ord],
xlab = "",
ylab = "",
type = "l",
col = 4,
lty = 2,
lwd = 1.5)
abline(a = 0, b = 0)
readline(prompt="Press [enter] to continue")
}
}
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Defines functions plot_impactsnopar
Documented in plot_impactsnopar
#' @name plot_impactsnopar
#' @rdname plot_impactsnopar
#'
#' @title Plot direct, indirect and total impacts functions
#' for continous non-parametric covariates in semiparametric spatial regression models.
#'
#' @description Plot direct, indirect and total impacts functions for
#' non-parametric covariates included in a semiparametric spatial
#' or spatio-temporal SAR model. This model must include a spatial
#' lag of the dependent variable (SAR) to have indirect effects
#' different from 0, otherwise, total and direct function effects
#' are the same. The effect functions can be smoothed to overcome
#' the instabilities created by the premultiplication of matrix
#' \eqn{(I - \rho W)^{-1}}
#'
#' @param impactsnopar object returned from \code{\link{impactsnopar}} function.
#' @param data dataframe with the data.
#' @param smooth logical value to choose smoothing of the effects function
#' prior to plot. Default TRUE.
#' @param span span for the kernel of the smoothing (see \code{\link{loess}}
#' for details). Default c(0.1, 0.1, 0.2).
#' @param 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`.
#' @param nt Number of temporal periods. It is needed
#' for dynamic models.
#'
#' @return plot of the direct, indirect and total impacts function for each non-parametric
#' covariate included in the object returned from \code{\link{impactsnopar}}.
#'
#' @author
#' \tabular{ll}{
#' Roman Minguez \tab \email{roman.minguez@@uclm.es} \cr
#' Roberto Basile \tab \email{roberto.basile@@univaq.it} \cr
#' Maria Durban \tab \email{mdurban@@est-econ.uc3m.es} \cr
#' Gonzalo Espana-Heredia \tab \email{gehllanza@@gmail.com} \cr
#' }
#'
#' @seealso
#' \itemize{
#' \item \code{\link{impactsnopar}} compute total, direct and indirect effect
#' functions for non-parametric continuous covariates.
#' \item \code{\link{fit_terms}} compute smooth functions for non-parametric
#' continuous covariates.
#' \item \code{\link{plot_terms}} plot the terms of non-parametric covariates.
#' }
#'
#' @references \itemize{
#' \item Basile, R.; Durban, M.; Minguez, R.; Montero, J. M.; and
#' Mur, J. (2014). Modeling regional economic dynamics: Spatial
#' dependence, spatial heterogeneity and nonlinearities.
#' \emph{Journal of Economic Dynamics and Control}, (48), 229-245.
#' <doi:10.1016/j.jedc.2014.06.011>
#' }
#'
#' @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$Longitude) == FALSE), ]
#'
#' form1 <- lnSale_Price ~ Fireplaces + Garage_Cars +
#' pspl(lnLot_Area, nknots = 20) +
#' pspl(lnTotal_Bsmt_SF, nknots = 20) +
#' pspl(lnGr_Liv_Area, nknots = 20)
#'
#' \donttest{
#' ########### Constructing the spatial weights matrix
#' coord_sf1 <- cbind(ames_sf1$Longitude, ames_sf1$Latitude)
#' k5nb <- knn2nb(knearneigh(coord_sf1, k = 5,
#' longlat = TRUE, use_kd_tree = FALSE), sym = TRUE)
#' lw_ames <- nb2listw(k5nb, style = "W",
#' zero.policy = FALSE)
#' gamsar <- pspatfit(form1, data = ames_sf1,
#' type = "sar", listw = lw_ames,
#' method = "Chebyshev")
#' summary(gamsar)
#' nparimpacts <- impactsnopar(gamsar, listw = lw_ames, viewplot = FALSE)
#' plot_impactsnopar(nparimpacts, data = ames_sf1, smooth = TRUE)
#' ###### Examples using a panel data of rate of
#' ###### unemployment for 103 Italian provinces in period 1996-2014.
#' library(pspatreg)
#' data(unemp_it)
#' ## Wsp_it is a matrix. Create a neighboord list
#' lwsp_it <- spdep::mat2listw(Wsp_it)
#' ## short sample for spatial pure case (2d)
#' ######## No Spatial Trend: PSAR including a spatial
#' ######## lag of the dependent variable
#' form1 <- unrate ~ partrate + agri + cons + empgrowth +
#' pspl(serv, nknots = 15)
#' gamsar <- pspatfit(form1, data = unemp_it,
#' type = "sar",
#' listw = lwsp_it)
#' summary(gamsar)
#' ###### Non-Parametric Total, Direct and Indirect impacts
#' imp_nparvar <- impactsnopar(gamsar, alpha = 0.05,
#' listw = lwsp_it,
#' viewplot = TRUE)
#' ##### This returns the same result but using plot_impactsnopar()
#' imp_nparvar <- impactsnopar(gamsar, listw = lwsp_it, alpha = 0.05,
#' viewplot = FALSE)
#' plot_impactsnopar(imp_nparvar, data = unemp_it,
#' smooth = TRUE)
#' }
#'
#' @export
plot_impactsnopar <- function(impactsnopar, data, smooth = TRUE,
span = c(0.1, 0.1, 0.2),
dynamic = FALSE,
nt = NULL) {
if (inherits(data, "sf"))
data <- st_drop_geometry(data)
nfull <- nrow(data)
if (dynamic) {
if (is.null(nt))
stop("plot_impactsnopar function needs nt as argument for dynamic models")
idxyear1 <- seq(from = 1, to = nfull, by = nt)
data <- data[-idxyear1, ]
}
tot <- impactsnopar$impnopar_tot
if (nrow(tot) != nrow(data))
stop("Dimensions of impacts and data disagree.
Likely the model is dynamic and arguments dynamic = TRUE and nt value
are needed in plot_impactsnopar")
uptot <- impactsnopar$impnopar_tot_up
lowtot <- impactsnopar$impnopar_tot_low
dir <- impactsnopar$impnopar_dir
updir <- impactsnopar$impnopar_dir_up
lowdir <- impactsnopar$impnopar_dir_low
ind <- impactsnopar$impnopar_ind
upind <- impactsnopar$impnopar_ind_up
lowind <- impactsnopar$impnopar_ind_low
for (i in 1:ncol(tot)) {
name_var <- colnames(tot)[i]
var <- as.matrix(data[, c(name_var)])
colnames(var) <- name_var
ord <- order(var)
tot_i <- matrix(tot[, c(name_var)], ncol = 1)
colnames(tot_i) <- name_var
uptot_i <- matrix(uptot[, c(name_var)], ncol = 1)
colnames(uptot_i) <- name_var
lowtot_i <- matrix(lowtot[, c(name_var)], ncol = 1)
colnames(lowtot_i) <- name_var
dir_i <- matrix(dir[, c(name_var)], ncol = 1)
colnames(dir_i) <- name_var
updir_i <- matrix(updir[,c(name_var)], ncol = 1)
colnames(updir_i) <- name_var
lowdir_i <- matrix(lowdir[,c(name_var)], ncol = 1)
colnames(lowdir_i) <- name_var
ind_i <- matrix(ind[,c(name_var)], ncol = 1)
colnames(ind_i) <- name_var
upind_i <- matrix(upind[,c(name_var)], ncol = 1)
colnames(upind_i) <- name_var
lowind_i <- matrix(lowind[,c(name_var)], ncol = 1)
colnames(lowind_i) <- name_var
if (smooth) {
span_tot <- span[1]
span_dir <- span[2]
span_ind <- span[3]
tot_i_smooth <- predict(loess(tot_i ~ var, span = span_tot),
method = "loess()")
uptot_i_smooth <- predict(loess(uptot_i ~ var, span = span_tot),
method = "loess()")
lowtot_i_smooth <- predict(loess(lowtot_i ~ var, span = span_tot),
method = "loess()")
if (sum(is.nan(tot_i_smooth)) > 1 ||
sum(is.nan(uptot_i_smooth)) > 1 ||
sum(is.nan(lowtot_i_smooth)) > 1 ) {
warning(paste("Smoothing of total impacts with variable ", name_var,
" produces NaN. ",
"This variable is not suited for smoothing \n", sep = ""))
} else {
tot_i <- tot_i_smooth
uptot_i <- uptot_i_smooth
lowtot_i <- lowtot_i_smooth
}
dir_i_smooth <- predict(loess(dir_i ~ var, span = span_dir),
method = "loess()")
updir_i_smooth <- predict(loess(updir_i ~ var, span = span_dir),
method = "loess()")
lowdir_i_smooth <- predict(loess(lowdir_i ~ var, span = span_dir),
method = "loess()")
if (sum(is.nan(dir_i_smooth)) > 1 ||
sum(is.nan(updir_i_smooth)) > 1 ||
sum(is.nan(lowdir_i_smooth)) > 1 ) {
warning(paste("Smoothing of direct impacts with variable ", name_var,
" produces NaN. ",
"This variable is not suited for smoothing \n", sep = ""))
} else {
dir_i <- dir_i_smooth
updir_i <- updir_i_smooth
lowdir_i <- lowdir_i_smooth
}
ind_i_smooth <- predict(loess(ind_i ~ var, span = span_ind),
method = "loess()")
upind_i_smooth <- predict(loess(upind_i~var, span = span_ind),
method = "loess()")
lowind_i_smooth <- predict(loess(lowind_i ~ var, span = span_ind),
method = "loess()")
if (sum(is.nan(ind_i_smooth)) > 1 ||
sum(is.nan(upind_i_smooth)) > 1 ||
sum(is.nan(lowind_i_smooth)) > 1 ) {
warning(paste("Smoothing of indirect impacts with variable ", name_var,
" produces NaN. ",
"This variable is not suited for smoothing \n", sep = ""))
} else {
ind_i <- ind_i_smooth
upind_i <- upind_i_smooth
lowind_i <- lowind_i_smooth
}
}
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar))
par(mfrow = c(1, 1))
plot(var[ord], tot_i[ord],
type = "l",
ylab = paste("f(", name_var, ")"),
xlab = name_var,
ylim = c(min(lowtot_i), max(uptot_i)),
cex.lab = 1.0,
col = 2,
lty = 1,
lwd = 2,
cex.main = 1.0,
main = paste("Total Impacts: ", name_var))
lines(var[ord], uptot_i[ord],
xlab = "", ylab = "",
type = "l", col = 2, lty = 2, lwd = 1.5)
lines(var[ord],lowtot_i[ord],
xlab = "",
ylab = "",
type = "l",
col = 2,
lty = 2,
lwd = 1.5)
abline(a = 0, b = 0)
readline(prompt="Press [enter] to continue")
plot(var[ord], dir_i[ord],
type = "l",
ylab = paste("f(", name_var, ")"),
xlab = name_var,
ylim = c(min(lowdir_i), max(updir_i)),
cex.lab = 1.0,
col = 3,
lty = 1,
lwd = 2,
cex.main = 1.0,
main = paste("Direct Impacts:", name_var))
lines(var[ord], updir_i[ord],
xlab = "",
ylab = "",
type = "l",
col = 3,
lty = 2,
lwd = 1.5)
lines(var[ord],lowdir_i[ord],
xlab = "",
ylab = "",
type = "l",
col = 3,
lty = 2,
lwd = 1.5)
abline(a = 0, b = 0)
readline(prompt="Press [enter] to continue")
plot(var[ord], ind_i[ord],
type = "l",
ylab = paste("f(", name_var, ")"),
xlab = name_var,
ylim = c(min(lowind_i), max(upind_i)),
cex.lab = 1.0,
col = 4,
lty = 1,
lwd = 2,
cex.main = 1.0,
main = paste("Indirect Impacts: ", name_var))
lines(var[ord], upind_i[ord],
xlab = "",
ylab = "",
type = "l",
col = 4,
lty = 2,
lwd = 1.5)
lines(var[ord], lowind_i[ord],
xlab = "",
ylab = "",
type = "l",
col = 4,
lty = 2,
lwd = 1.5)
abline(a = 0, b = 0)
readline(prompt="Press [enter] to continue")
}
}
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