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"
)
#>
#> ====================================================
#> senseR STRICT - Proxy Indicator Diagnostic
#> ====================================================
#> Target construct : gdp
#>
#> ----------------------------------------------------
#> Proxy : ntl
#> ----------------------------------------------------
#> 1. Monotonicity (Spearman) : 0.0039
#> 2. Information Content (R2) : 0.0025
#> 3. Elasticity (Responsiveness) : 0.0495 | Score: 0.3 | Penalized if < 0.1
#> 4. Coefficient of Variation (CV) : 0.1976 | Score: 1 | Penalized if < 0.02
#> 5. Average Absolute Change : 11.2733 | Score: 1 | Penalized if < 0.01
#> 6. Ceiling Effect Ratio : 0.5937 | Score: 1 | Penalized if > 0.95
#> 7. Stability (Beta Consistency) : 0
#> Hard Failure Triggered : FALSE
#> ----------------------------------------------------
#> Final Proxy Score (Median Rule) : 0.3
#> ----------------------------------------------------
#> Proxy : ntl
#> Score : 0.3
#> Status: Not suitable proxy
#> Interpretation:
#> The proxy fails to provide reliable and informative representation.
#>
#> ----------------------------------------------------
#> Proxy : road_density
#> ----------------------------------------------------
#> 1. Monotonicity (Spearman) : 0.1223
#> 2. Information Content (R2) : 0.0167
#> 3. Elasticity (Responsiveness) : 0.1292 | Score: 1 | Penalized if < 0.1
#> 4. Coefficient of Variation (CV) : 0.1552 | Score: 1 | Penalized if < 0.02
#> 5. Average Absolute Change : 0.5082 | Score: 1 | Penalized if < 0.01
#> 6. Ceiling Effect Ratio : 0.738 | Score: 1 | Penalized if > 0.95
#> 7. Stability (Beta Consistency) : 0.3952
#> Hard Failure Triggered : FALSE
#> ----------------------------------------------------
#> Final Proxy Score (Median Rule) : 1
#> ----------------------------------------------------
#> Proxy : road_density
#> Score : 1
#> Status: Suitable proxy
#> Interpretation:
#> This proxy demonstrates adequate variability, responsiveness, and stability.
#>
#> ----------------------------------------------------
#> Proxy : mobile_signal
#> ----------------------------------------------------
#> 1. Monotonicity (Spearman) : 0.0987
#> 2. Information Content (R2) : 0.0019
#> 3. Elasticity (Responsiveness) : 0.0441 | Score: 0.3 | Penalized if < 0.1
#> 4. Coefficient of Variation (CV) : 0.2243 | Score: 1 | Penalized if < 0.02
#> 5. Average Absolute Change : 18.5466 | Score: 1 | Penalized if < 0.01
#> 6. Ceiling Effect Ratio : 0.6397 | Score: 1 | Penalized if > 0.95
#> 7. Stability (Beta Consistency) : 0.7629
#> Hard Failure Triggered : FALSE
#> ----------------------------------------------------
#> Final Proxy Score (Median Rule) : 0.7629
#> ----------------------------------------------------
#> Proxy : mobile_signal
#> Score : 0.763
#> Status: Suitable proxy
#> Interpretation:
#> This proxy demonstrates adequate variability, responsiveness, and stability.
#>
#> ====================================================
#> Scientific References:
#>
#> [1] Spearman, C. (1904). The proof and measurement of association between two things.
#> American Journal of Psychology, 15(1), 72-101.
#>
#> [2] Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences.
#>
#> [3] Wooldridge, J. M. (2013). Introductory Econometrics: A Modern Approach.
#>
#> [4] OECD (2008). Handbook on Constructing Composite Indicators:
#> Methodology and User Guide.
#>
#> [5] Hamilton, J. D. (1994). Time Series Analysis.
#>
#> [6] Chow, G. C. (1960). Tests of equality between sets of coefficients
#> in two linear regressions. Econometrica.# Indonesian language support
senser(
data = df,
proxy = c("ntl", "road_density"),
target = "gdp",
lang = "indonesia"
)
#>
#> ====================================================
#> senseR STRICT - Proxy Indicator Diagnostic
#> ====================================================
#> Konstruk target : gdp
#>
#> ----------------------------------------------------
#> Proxy : ntl
#> ----------------------------------------------------
#> 1. Monotonisitas (Spearman) : 0.0039
#> 2. Kandungan Informasi (R2) : 0.0025
#> 3. Elastisitas (Responsivitas) : 0.0495 | Skor: 0.3 | Penalti jika < 0.1
#> 4. Koefisien Variasi (KV) : 0.1976 | Skor: 1 | Penalti jika < 0.02
#> 5. Rata-rata Perubahan Absolut : 11.2733 | Skor: 1 | Penalti jika < 0.01
#> 6. Rasio Efek Ceiling : 0.5937 | Skor: 1 | Penalti jika > 0.95
#> 7. Stabilitas (Konsistensi Beta) : 0
#> Kegagalan Struktural Terdeteksi : FALSE
#> ----------------------------------------------------
#> Skor Akhir Proxy (Aturan Median) : 0.3
#> ----------------------------------------------------
#> Proxy : ntl
#> Score : 0.3
#> Status: Proxy tidak layak
#> Interpretation:
#> Proxy gagal merepresentasikan konstruk secara informatif dan andal.
#>
#> ----------------------------------------------------
#> Proxy : road_density
#> ----------------------------------------------------
#> 1. Monotonisitas (Spearman) : 0.1223
#> 2. Kandungan Informasi (R2) : 0.0167
#> 3. Elastisitas (Responsivitas) : 0.1292 | Skor: 1 | Penalti jika < 0.1
#> 4. Koefisien Variasi (KV) : 0.1552 | Skor: 1 | Penalti jika < 0.02
#> 5. Rata-rata Perubahan Absolut : 0.5082 | Skor: 1 | Penalti jika < 0.01
#> 6. Rasio Efek Ceiling : 0.738 | Skor: 1 | Penalti jika > 0.95
#> 7. Stabilitas (Konsistensi Beta) : 0.3952
#> Kegagalan Struktural Terdeteksi : FALSE
#> ----------------------------------------------------
#> Skor Akhir Proxy (Aturan Median) : 1
#> ----------------------------------------------------
#> Proxy : road_density
#> Score : 1
#> Status: Proxy layak
#> Interpretation:
#> Proxy memiliki variabilitas, sensitivitas, dan stabilitas yang memadai.
#>
#> ====================================================
#> Referensi Ilmiah:
#>
#> [1] Spearman, C. (1904). The proof and measurement of association between two things.
#> American Journal of Psychology, 15(1), 72-101.
#>
#> [2] Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences.
#>
#> [3] Wooldridge, J. M. (2013). Introductory Econometrics: A Modern Approach.
#>
#> [4] OECD (2008). Handbook on Constructing Composite Indicators:
#> Methodology and User Guide.
#>
#> [5] Hamilton, J. D. (1994). Time Series Analysis.
#>
#> [6] Chow, G. C. (1960). Tests of equality between sets of coefficients
#> in two linear regressions. Econometrica.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.