senseR-introduction
In senseR: Proxy Indicator Diagnostic Tool for Analytical and Policy Use

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(senseR)

Introduction

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.

The `senser()` Function

The 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"
)

# Indonesian language support
senser(
  data = df,
  proxy = c("ntl", "road_density"),
  target = "gdp",
  lang = "indonesia"
)

Notes

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.

References

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.