tscsEstimate {TSCS}

The Second Step of TSCS for 2D Rectangular Grid System - Estimation

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

tscsEstimate(matrix, newdata, h, v)

Arguments

matrix

data frame; the first return value coef_matrix of function tscsRegression in the first step of TSCS.

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)

h

numeric; side length of the unit grid in X coordinate direction.

v

numeric; side length of the unit grid in Y coordinate direction.

Details

• The first step of TSCS spatial interpolation should be carried out by function tscsRegression, which is the prerequisite of tscsEstimate.

• For 3D rectangular grid system, the procedure of TSCS stays the same. Please see tscsRegression3D and tscsEstimate3D.

• 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 3 variables in order: X coordinate, Y 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

tscsRegression, tscsEstimate3D, plot_NA, plot_map

Examples

## 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)