appraisal_index: Compute Appraisal Index of Interpolation/Prediction Result

In TSCS: Time Series Cointegrated System

Description

Two appraisal indexes used for evaluating the result of interpolation/prediction - RMSE and standard deviation of error.

Usage

appraisal_index(est, true)

Arguments

est	a numeric vector; estimations.
true	a numeric vector; true values.

Details

• The first appraisal index is RMSE, abbr. of root-mean-square error. It is used for measuring the differences between estimated values by a method and the values actually observed. Smaller RMSE means more accurate interpolation/prediction.

• The second appraisal index is standard deviation of error, which is used for measuring how far the errors are spread out from their mean, namely, stability of errors. Smaller value means greater stability of errors, suggesting that errors would not fluctuate heavily due to difference of data.

Value

A list of 2 is returned, including:

RMSE

numeric; RMSE.

std

numeric; standard deviation of error.

See Also

plot_compare

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)

TSCS documentation built on May 2, 2019, 4:04 p.m.