Description Usage Arguments Details Value See Also Examples
View source: R/namespace.R View source: R/tscsEstimate3D.R
tscsEstimate
estimates the missing observations within the cross-section data (pure spatial data)
of a particular time point you have selected, namely, the interpolation process.
1 | tscsEstimate3D(matrix, newdata, h1, h2, v)
|
matrix |
data frame; the first return value |
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) |
h1 |
numeric; side length of the unit cubic grid in X coordinate direction (horizontal). |
h2 |
numeric; side length of the unit cubic grid in Y coordinate direction (horizontal). |
v |
numeric; side length of the unit cubic grid in Z coordinate direction (vertical). |
The first step of TSCS spatial interpolation should be carried out by function tscsRegression3D
,
which is the prerequisite of tscsEstimate3D
.
For 2D rectangular grid system, the procedure of TSCS stays the same.
Please see tscsRegression
and tscsEstimate
.
Attentions: Since TSCS is only capable of interpolation but not extrapolation, please make sure that the missing observations in a given spatial domain are all located at interior spatial locations. Otherwise, extrapolation would occur with an error following.
A list of 3 is returned, including:
estimate
data frame; estimate of missing observations which contains the 4 variables in order: X coordinate, Y coordinate, Z coordinate and estimation.
complete
data frame; an updated version of the cross-section data (pure spatial data) newdata
,
with all of its missing observations interpolated.
NA_id
an integer vector; reveals the instance ID, in data frame newdata
,
of spatial locations with missing observation.
tscsRegression3D
, tscsEstimate
, plot3D_NA
, plot3D_map
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 <- 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)
|
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