TSCS (version 0.1.1)

plot_compare: Graphic Comparison Between Estimates and True Values

Description

Provided that you have the true values of missing observations, you can compare them with the results of interpolation. plot_compare visualizes the comparison between estimates and true values. (NB: this plotting function can also be used in other similar situations involving comparison between estimates and true values.)

Usage

plot_compare(est, true, cex = 1, width = 1, P = 6/7, AI = TRUE)

Arguments

est

a numeric vector; estimations.

true

a numeric vector; true values.

cex

numeric; size of point to be plotted. (default: 1)

width

numeric; width of fitted straight line. (default: 1)

P

numeric, between 0 and 1; position for superimposing values of appraisal indexes. (default: 6/7)

AI

logical; TRUE for presenting appraisal indexes while FALSE for not. (default: TRUE)

Details

Attentions:

• The values in est and true vectors should be arranged in the same order, in correspondence with the sequence of observations.

• If the maximum value of either est or true is greater than 1000, or the minimum is smaller than -1000, please make appropriate transformation that limits your data to bound [-1000,1000].

In the plot:

• The big red point is the origin.

• The red line stands for straight line y = x.

• The blue line stands for fitted straight line.

See Also

appraisal_index

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
# }