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ffplot <- function (t,y,skeleton,fcast,std.error) {
#*********************************************************************
# This function makes a plot of the flow field forecast
#
# Input: t - time series observation times
# y - time series response values
# skeleton - Matrix containing the data skeleton
# fcast - vector holding the forecast values
# std.error - vector holding the standard errors
#
# Output: Plot of times series and forecast values
#
# References: None
#
#**********************************************************************
par(mai=c(1.1,1.0,0.9,1.0))
knots <- length(skeleton$sd)-4
space <- skeleton[knots+2,2]
lknot <- skeleton[knots+3,2]
fknot <- skeleton[knots+3,1]
steps <- length(fcast)
kvector <- seq(fknot-space,lknot,space)
tf <- rep(0,steps)
for (i in 1:steps){
tf[i] <-lknot + i*steps
}
sd <- skeleton$sd[1:(knots+1)]
xmin <- (fknot - space)
xmax <- lknot + steps*space
ymin <- min(y)
ymax <- max(y)
# Plot the raw time series data
plot(t,y,pch=20,bg="black",xlim=c(xmin,xmax),ylim=c(ymin,ymax),bty='L',main="Flow Field Forecast")
# Plot the forecast values
points(tf,fcast,pch=21,col="blue")
# Plot the penalized spline regression
points(kvector,sd,type="l",col="red")
# Plot the error bands
points(tf,fcast+2*std.error,type="l",col="green")
points(tf,fcast-2*std.error,type="l",col="green")
legend("topright", inset=c(0,0),c("TS Data","Forecast","PSR","+/- 2SE"),cex=0.8,col=c("black","blue","red","green"),bty="n",pch=c(16,1,NA,NA),lty=c(NA,NA,1,1))
}
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