Description Usage Arguments Details See Also Examples
View source: R/namespace.R View source: R/plot_NA.R
plot_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.
1 |
newdata |
data frame; should only contain the three variables in order: X coordinate, Y 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. |
title |
a main title for the plot. |
cex |
numeric; size of plotting point for each spatial location. (default: 1) |
plot_NA
is exclusive to 2D rectangular grid system. Similarly, if you want to fathom how this package
handles 3D rectangular grid system, please refer to plot3D_NA
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## Not run:
## TSCS spatial interpolation procedure:
basis <- tscsRegression(data = data, h = 1, v = 1, alpha = 0.01); # regression
basis$percentage # see the percentage of cointegrated relationships
est <- tscsEstimate(matrix = basis$coef_matrix, newdata = newdata, h = 1, v = 1); # estimation
str(est)
## comparison of estimates and true values:
plot_compare(est = est$estimate[,3], true = true) # graphic comparison
index <- appraisal_index(est = est$estimate[,3], true = true); # RMSE & std
index
## data visualization:
plot_dif(data = data[,1:2], h = 1, v = 1) # differentiate boundary and interior spatial locations
plot_NA(newdata = newdata) # show spatial locations with missing value, for a cross-section data
plot_map(newdata = newdata) # plot the 2D spatial map, for a cross-section data
## End(Not run)
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