tscsEstimate3D: The Second Step of TSCS for 3D Rectangular Grid System -...

In TSCS: Time Series Cointegrated System

Description

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.

Usage

tscsEstimate3D(matrix, newdata, h1, h2, v)

Arguments

matrix	data frame; the first return value coef_matrix of function tscsRegression3D in the first step of TSCS.
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).

Details

• 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.

Value

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.

See Also

tscsRegression3D, tscsEstimate, plot3D_NA, plot3D_map

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.