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senseR is a statistical diagnostic tool designed to
evaluate whether proxy indicators can reliably represent an underlying
construct that cannot be directly observed or measured. It is intended
for analytical diagnostics and policy-oriented assessment. Note that it
does not perform causal inference.
senser() FunctionThe main function is senser(). It computes a diagnostic score for each proxy based on five components: Monotonicity – Spearman rank correlation between proxy and target. Information content – Proportion of variance explained (R-squared). Stability – Sensitivity of regression coefficients across subsamples. Distributional alignment – Similarity of standardized distributions via Kolmogorov–Smirnov test. Bias risk – Penalization for strong nonlinearity indicating potential proxy distortion.
The overall score is the average of these five components, ranging from 0 to 1: Suitable proxy: score >= 0.70 Conditionally suitable: 0.40 <= score < 0.70 Not suitable proxy: score < 0.40 Interpretation is automatically generated in English or Indonesian.
# example
set.seed(123)
# Simulated dataset
df <- data.frame(
gdp = rnorm(100, 10, 2),
ntl = rnorm(100, 50, 10),
road_density = rnorm(100, 3, 0.5),
mobile_signal = rnorm(100, 70, 15)
)
# Run senser in English
senser(
data = df,
proxy = c("ntl", "road_density", "mobile_signal"),
target = "gdp",
lang = "english"
)
#> target proxy monotonicity information_content elasticity cv
#> X gdp ntl 0.00390039 0.002453434 0.04953215 0.1976486
#> X1 gdp road_density 0.12232823 0.016686441 0.12917601 0.1551972
#> X2 gdp mobile_signal 0.09872187 0.001942958 0.04407900 0.2243373
#> avg_change ceiling_ratio stability_score final_score classification
#> X 11.2733208 0.5936694 0.0000000 0.3000000 Not suitable proxy
#> X1 0.5081511 0.7380208 0.3952459 1.0000000 Suitable proxy
#> X2 18.5465958 0.6397297 0.7629491 0.7629491 Suitable proxy
#> interpretation
#> X Proxy lacks sufficient statistical reliability.
#> X1 Proxy demonstrates adequate variability and statistical stability.
#> X2 Proxy demonstrates adequate variability and statistical stability.# Indonesian language support
senser(
data = df,
proxy = c("ntl", "road_density"),
target = "gdp",
lang = "indonesia"
)
#> target proxy monotonicity information_content elasticity cv
#> X gdp ntl 0.00390039 0.002453434 0.04953215 0.1976486
#> X1 gdp road_density 0.12232823 0.016686441 0.12917601 0.1551972
#> avg_change ceiling_ratio stability_score final_score classification
#> X 11.2733208 0.5936694 0.0000000 0.3 Proxy tidak layak
#> X1 0.5081511 0.7380208 0.3952459 1.0 Proxy layak
#> interpretation
#> X Proxy tidak cukup andal secara statistik.
#> X1 Proxy memiliki variabilitas dan stabilitas yang memadai.Output is printed to the console; the function does not return a value invisibly. Designed for applied diagnostics and policy assessment. Always compare multiple proxies to select the most reliable indicators.
Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro-level estimation of poverty and inequality. Econometrica. Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growth from outer space. American Economic Review.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.