plot3D_NA: Visualize the Spatial Distribution of Missing Observations -...

Description Usage Arguments Details See Also Examples

View source: R/namespace.R View source: R/plot3D_NA.R

Description

plot3D_NA shows spatial locations with or without missing observation. It is plotted based on the cross-section data of a given time point, which is also often extracted from spatio-temporal data.

Usage

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plot3D_NA(newdata, xlab = NULL, ylab = NULL, zlab = NULL, title = NULL,
  cex = 3, color = "orange", colorNA = "blue")

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 location. (default: 3)

color

colour to be used to fill the spatial locations. (default: "orange")

colorNA

colour for denoting missing values/observations. (default: "blue")

Details

See Also

plot_NA, plot3D_map, plot3D_dif

Examples

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## 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.

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