ggmap_KS {SpatFD}

Map plot of a 'KS_pred' object

Description

A visualization of the predicted kriging in a colormap for a specific window time.

Usage

ggmap_KS(KS, map_path, window_time = NULL, method = "lambda", map_n = 5000,
zmin = NULL, zmax = NULL, graph = "plotly")

Arguments

KS

Object of class 'KS_pred'. Not NULL.

map_path

Character indicating the directory of the shape file or an object of class SpatialPolygonDataFrame load from a shape file by rgdal::readOGR. If NULL the prediction is plot in a rectangle. Default NULL.

window_time

numeric. Vector of window time to see the spatial prediction. If NULL choose the range values of KS$SFD. Default NULL.

method

character. "lambda" or "scores". Default "lambda".

map_n

numeric. Number of points to sample in the map. Default 5000.

zmin

numeric. Minimum value predicted for the color scale. If NULL is chosen from the data. Default NULL.

zmax

numeric. Maximum value predicted for the color scale. If NULL is chosen from the data. Default NULL.

graph

character. "plotly" or "gg" whether to use plotly or ggplot graphics. Default "plotly".

Value

Plotly or ggplot image

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=100),Y=seq(97000,112000,len=100))

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)

ggmap_KS(KS_SFD_PM10_both,
         map_path = map,
         window_time = c(5108,5109,5110),
         method = "scores",
         zmin = 50,
         zmax = 120)