plot3D_map: Visualize Spatial(Cross-Section) Data of a Given Time Point -...

In TSCS: Time Series Cointegrated System

Description

plot_map draws a three-dimensional spatial map. It is plotted based on the cross-section data of a given time point, which is also often extracted from spatio-temporal data.

Usage

plot3D_map(newdata, xlab = NULL, ylab = NULL, zlab = NULL, title = NULL,
  cex = 9, colorNA = "white")

Arguments

newdata	data frame; should only contain the four variables in order: X coordinate, Y coordinate, Z coordinate and observation. This is the cross-section data or pure spatial data of a particular time point you have selected, with missing observations that you want to predict. (coordinates must be numeric)
xlab	a label for the x axis, defaults to the name of X coordinate.
ylab	a label for the y axis, defaults to the name of Y coordinate.
zlab	a label for the z axis, defaults to the name of Z coordinate.
title	a main title for the plot.
cex	numeric; size of plotting point for each spatial locations. (default: 9)
colorNA	colour for missing values/observations. (default: "white")

Details

• The resulting plot is interactive.

• plot3D_map is exclusive to 3D rectangular grid system. Similarly, if you want to fathom how this package handles 2D rectangular grid system, please refer to plot_map.

See Also

plot_map, plot3D_NA, plot3D_dif

Examples

## Not run: 

## TSCS spatial interpolation procedure:

basis <- tscsRegression3D(data = data, h1 = 3.75, h2 = 2.5, v = 5, alpha = 0.01);
basis$percentage
est <- tscsEstimate3D(matrix = basis$coef_matrix, newdata = newdata, h1 = 3.75, h2 = 2.5, v = 5);
str(est)

## comparison of estimates and true values:

plot_compare(est = est$estimate[,4], true = true)
index <- appraisal_index(est = est$estimate[,4], true = true);
index

## data visualization:

plot3D_dif(data = data[,1:3], h1 = 3.75, h2 = 2.5, v = 5)
plot3D_NA(newdata = newdata)
plot3D_map(newdata = newdata)

## End(Not run)

TSCS documentation built on May 2, 2019, 4:04 p.m.