ggplot_KS {SpatFD}

ggplot of predicted functions

Description

Plot with or without predicted variance in each spatial location of the functional kriging.

Usage

ggplot_KS(KS, show.varpred = FALSE, main = "Functional Data",
main2 = "Functional Data", ylab = "Value", xlab = "Time", ndigits = 2,
palette.plot = c("#440154FF", "#3336FF", "#33FCFF", "#33FF4C", "#FDE725FF"))

Arguments

KS

Object of class 'KS_pred'. Not NULL.

show.varpred

Boolean. If the predicted variance is shown. Default FALSE.

main

character. Title of the plot.Default "Functional Data".

main2

character. If there are two methods where used, the title of the second plot. Default "Functional Data".

ylab

character. Name of the y-axis.

xlab

character. Name of the x-axis.

ndigits

numeric. Number of decimals for the predicted variance if shown. Default 2.

palette.plot

list. String values of hexadecimal colors. Default c("#440154FF", "#3336FF", "#33FCFF", "#33FF4C", "#FDE725FF")

Value

ggplot. If there are two plots a list of ggplots.

Author(s)

Diego Sandoval [e-mail].

References

Bohorquez, M., Giraldo, R., & Mateu, J. (2016). Multivariate functional random fields: prediction and optimal sampling. Stochastic Environmental Research and Risk Assessment, 31, pages53–70 (2017).

See Also

KS_scores_lambdas

Examples

library(gstat)
data(AirQualityBogota)
newcoorden=data.frame(X=seq(93000,105000,len=10),Y=seq(97000,112000,len=10))
SFD_PM10 <- SpatFD(PM10, coords = coord[, -1], basis = "Bsplines", nbasis = 17,
norder=5, lambda = 0.00002, nharm=3)
modelos <- list(vgm(psill = 2634000, "Exp", range = 2103.25, nugget =  0),
                vgm(psill = 101494.96, "Exp", range = 1484.57, nugget = 0),
                vgm(psill =53673, "Exp", range = 42406, nugget =  0))

KS_SFD_PM10_both <- KS_scores_lambdas(SFD_PM10, newcoorden, method = "both",
model = modelos)

ggplot_KS(KS_SFD_PM10_both,show.varpred = FALSE,
          main = "Plot 1 - Using Scores",
          main2 = "Plot 2 - Using Lambda",
          ylab = "PM10")