Matríz de Correlación
library(corrplot)
## Warning: package 'corrplot' was built under R version 4.5.3
## corrplot 0.95 loaded
library(dplyr)
##
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(plm)
## Warning: package 'plm' was built under R version 4.5.3
##
## Adjuntando el paquete: 'plm'
## The following objects are masked from 'package:dplyr':
##
## between, lag, lead
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
library(readxl)
Base_de_Datos_RStudio <- read_excel("C:/Users/Javie/OneDrive/Escritorio/Servicio Social/Análisis Econométrico/Base de Datos RStudio.xlsx")
View(Base_de_Datos_RStudio)
delitos_incidencias <- Base_de_Datos_RStudio %>%
select(-Municipio, -Ano)
Modelado de la matríz
matriz_cor <- cor(delitos_incidencias
, use = "complete.obs", method = "pearson")
corrplot(matriz_cor,
method = "circle",
type = "upper",
tl.col = "black",
tl.cex = 0.5,
tl.srt = 45)
cor_desapariciones <- as.data.frame(matriz_cor) %>%
select(Desaparicion_FGE, Desaparicion_CNB_2025) %>%
arrange(desc(Desaparicion_FGE))
print("=== TABLA DE CORRELACIONES CON DESAPARICIONES ===")
## [1] "=== TABLA DE CORRELACIONES CON DESAPARICIONES ==="
print(cor_desapariciones)
## Desaparicion_FGE Desaparicion_CNB_2025
## Desaparicion_FGE 1.000000e+00 0.44662607
## Robo_4R_V 4.856025e-01 0.26098739
## Violencia_familiar 4.673358e-01 0.19930066
## Desaparicion_CNB_2025 4.466261e-01 1.00000000
## Violacion_simple 4.143297e-01 0.32440414
## Robo_transportista_T 4.094508e-01 0.33734136
## Robo_transportista_V 4.042924e-01 0.33513757
## Abuso_sexual 3.787078e-01 0.15240783
## Homicidio_D_T 3.726360e-01 0.27887036
## Despojo 3.046520e-01 0.18068536
## Violacion_equiparada 2.573115e-01 0.33758931
## Robo_4_R_T 1.987441e-01 0.07441482
## Extorsion 1.935324e-01 0.02590343
## Acoso_sexual 1.727916e-01 -0.05990572
## Homicidio_D_A_F 1.718341e-01 0.04049455
## Feminicidio_FGE 7.045852e-02 0.23413729
## Secuestro_EEO 5.094281e-02 -0.02050013
## Corrupcion_Trafico_M_R 4.322326e-02 0.01472280
## Narcomenudeo 2.497512e-02 -0.06827746
## Masacres 1.781731e-05 -0.06722675
## Tomas_Huachicol_G_GLP -1.172757e-01 -0.12591301
## Ejecucion_extrajudicial -1.210853e-01 -0.04769618
## Trata_T_P -1.341514e-01 -0.07678155
Modelo de Panel de Efectos Fijos
panel_datos <- pdata.frame(Base_de_Datos_RStudio, index = c("Municipio", "Ano"))
Modelo FGE: Desaparición Fiscalía contra todos los posibles
delitos
names(Base_de_Datos_RStudio)
## [1] "Municipio" "Ano"
## [3] "Homicidio_D_T" "Homicidio_D_A_F"
## [5] "Feminicidio_FGE" "Masacres"
## [7] "Ejecucion_extrajudicial" "Desaparicion_FGE"
## [9] "Desaparicion_CNB_2025" "Secuestro_EEO"
## [11] "Corrupcion_Trafico_M_R" "Trata_T_P"
## [13] "Violencia_familiar " "Abuso_sexual"
## [15] "Acoso_sexual" "Violacion_simple"
## [17] "Violacion_equiparada" "Tomas_Huachicol_G_GLP"
## [19] "Robo_4_R_T" "Robo_4R_V"
## [21] "Robo_transportista_T" "Robo_transportista_V"
## [23] "Extorsion" "Despojo"
## [25] "Narcomenudeo"
modelo_fge <- plm(Desaparicion_FGE ~ Homicidio_D_T + Homicidio_D_A_F + Feminicidio_FGE +
Masacres + Ejecucion_extrajudicial + Secuestro_EEO +
Corrupcion_Trafico_M_R + Trata_T_P + Violencia_familiar. +
Abuso_sexual + Acoso_sexual + Violacion_simple +
Violacion_equiparada + Tomas_Huachicol_G_GLP +
Robo_4_R_T + Robo_4R_V +
Robo_transportista_T + Robo_transportista_V +
Extorsion + Despojo + Narcomenudeo,
data = panel_datos,
model = "within")
Modelo CNB: Desaparición CNB contra todos los posibles
delitos
modelo_cnb <- plm(Desaparicion_CNB_2025 ~ Homicidio_D_T + Homicidio_D_A_F + Feminicidio_FGE +
Masacres + Ejecucion_extrajudicial + Secuestro_EEO +
Corrupcion_Trafico_M_R + Trata_T_P + Violencia_familiar. +
Abuso_sexual + Acoso_sexual + Violacion_simple +
Violacion_equiparada + Tomas_Huachicol_G_GLP +
Robo_4_R_T + Robo_4R_V +
Robo_transportista_T + Robo_transportista_V +
Extorsion + Despojo + Narcomenudeo,
data = panel_datos,
model = "within")
summary(modelo_fge)
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = Desaparicion_FGE ~ Homicidio_D_T + Homicidio_D_A_F +
## Feminicidio_FGE + Masacres + Ejecucion_extrajudicial + Secuestro_EEO +
## Corrupcion_Trafico_M_R + Trata_T_P + Violencia_familiar. +
## Abuso_sexual + Acoso_sexual + Violacion_simple + Violacion_equiparada +
## Tomas_Huachicol_G_GLP + Robo_4_R_T + Robo_4R_V + Robo_transportista_T +
## Robo_transportista_V + Extorsion + Despojo + Narcomenudeo,
## data = panel_datos, model = "within")
##
## Balanced Panel: n = 8, T = 12, N = 96
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -17.27492 -5.22875 -0.96998 3.53068 31.48807
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## Homicidio_D_T 0.5417087 0.2377732 2.2783 0.02591 *
## Homicidio_D_A_F -0.6609710 0.2759173 -2.3955 0.01940 *
## Feminicidio_FGE -0.2479671 0.8358275 -0.2967 0.76763
## Masacres -0.2899287 0.8072008 -0.3592 0.72059
## Ejecucion_extrajudicial -0.9097922 1.0507353 -0.8659 0.38966
## Secuestro_EEO 0.7033399 1.2550511 0.5604 0.57707
## Corrupcion_Trafico_M_R -2.4149118 3.0447160 -0.7931 0.43049
## Trata_T_P -0.3358298 0.8444841 -0.3977 0.69213
## Violencia_familiar. 0.0057553 0.0322120 0.1787 0.85874
## Abuso_sexual 0.3261146 0.1964996 1.6596 0.10167
## Acoso_sexual 0.1204563 0.3428496 0.3513 0.72644
## Violacion_simple 0.3484759 0.2655438 1.3123 0.19389
## Violacion_equiparada -0.0790166 0.2752617 -0.2871 0.77495
## Tomas_Huachicol_G_GLP -0.0080492 0.0083369 -0.9655 0.33777
## Robo_4_R_T 0.0129458 0.0103798 1.2472 0.21666
## Robo_4R_V 0.0209846 0.0216112 0.9710 0.33504
## Robo_transportista_T 0.7460315 0.5620800 1.3273 0.18892
## Robo_transportista_V -0.7389934 0.5663601 -1.3048 0.19642
## Extorsion -0.0834232 0.4370061 -0.1909 0.84918
## Despojo 0.1093210 0.1341689 0.8148 0.41807
## Narcomenudeo -0.0533754 0.0575557 -0.9274 0.35706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 11832
## Residual Sum of Squares: 6202.5
## R-Squared: 0.47578
## Adj. R-Squared: 0.2567
## F-statistic: 2.89561 on 21 and 67 DF, p-value: 0.00051405
summary(modelo_cnb)
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = Desaparicion_CNB_2025 ~ Homicidio_D_T + Homicidio_D_A_F +
## Feminicidio_FGE + Masacres + Ejecucion_extrajudicial + Secuestro_EEO +
## Corrupcion_Trafico_M_R + Trata_T_P + Violencia_familiar. +
## Abuso_sexual + Acoso_sexual + Violacion_simple + Violacion_equiparada +
## Tomas_Huachicol_G_GLP + Robo_4_R_T + Robo_4R_V + Robo_transportista_T +
## Robo_transportista_V + Extorsion + Despojo + Narcomenudeo,
## data = panel_datos, model = "within")
##
## Balanced Panel: n = 8, T = 12, N = 96
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -9.271480 -2.223942 -0.029419 1.715805 16.591512
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## Homicidio_D_T 0.42305168 0.10721063 3.9460 0.0001930 ***
## Homicidio_D_A_F -0.46952964 0.12440956 -3.7741 0.0003429 ***
## Feminicidio_FGE 0.83724836 0.37686998 2.2216 0.0296940 *
## Masacres -0.76384413 0.36396235 -2.0987 0.0396166 *
## Ejecucion_extrajudicial -0.03541360 0.47377067 -0.0747 0.9406376
## Secuestro_EEO 0.32385448 0.56589553 0.5723 0.5690431
## Corrupcion_Trafico_M_R -1.04013153 1.37284547 -0.7576 0.4513192
## Trata_T_P 0.04511565 0.38077318 0.1185 0.9060384
## Violencia_familiar. -0.02381122 0.01452423 -1.6394 0.1058146
## Abuso_sexual 0.02748202 0.08860057 0.3102 0.7573878
## Acoso_sexual -0.26331313 0.15458895 -1.7033 0.0931446 .
## Violacion_simple 0.29730224 0.11973220 2.4831 0.0155359 *
## Violacion_equiparada 0.24698649 0.12411399 1.9900 0.0506743 .
## Tomas_Huachicol_G_GLP -0.00749022 0.00375905 -1.9926 0.0503834 .
## Robo_4_R_T 0.00142775 0.00468018 0.3051 0.7612639
## Robo_4R_V 0.00123656 0.00974437 0.1269 0.8994000
## Robo_transportista_T 0.23684643 0.25343873 0.9345 0.3533859
## Robo_transportista_V -0.22850215 0.25536861 -0.8948 0.3741008
## Extorsion -0.12847369 0.19704361 -0.6520 0.5166276
## Despojo 0.06553748 0.06049599 1.0833 0.2825426
## Narcomenudeo -0.00015981 0.02595154 -0.0062 0.9951049
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2846.5
## Residual Sum of Squares: 1261
## R-Squared: 0.55699
## Adj. R-Squared: 0.37186
## F-statistic: 4.01138 on 21 and 67 DF, p-value: 7.3729e-06
stargazer(modelo_fge, modelo_cnb,
type = "text",
title = "Contraste FGE vs CNB: Relación con todos los delitos",
column.labels = c("Fiscalía (FGE)", "CNB (2025)"),
dep.var.labels.include = FALSE,
model.numbers = FALSE)
##
## Contraste FGE vs CNB: Relación con todos los delitos
## ======================================================
## Dependent variable:
## ----------------------------
## Fiscalía (FGE) CNB (2025)
## ------------------------------------------------------
## Homicidio_D_T 0.542** 0.423***
## (0.238) (0.107)
##
## Homicidio_D_A_F -0.661** -0.470***
## (0.276) (0.124)
##
## Feminicidio_FGE -0.248 0.837**
## (0.836) (0.377)
##
## Masacres -0.290 -0.764**
## (0.807) (0.364)
##
## Ejecucion_extrajudicial -0.910 -0.035
## (1.051) (0.474)
##
## Secuestro_EEO 0.703 0.324
## (1.255) (0.566)
##
## Corrupcion_Trafico_M_R -2.415 -1.040
## (3.045) (1.373)
##
## Trata_T_P -0.336 0.045
## (0.844) (0.381)
##
## Violencia_familiar. 0.006 -0.024
## (0.032) (0.015)
##
## Abuso_sexual 0.326 0.027
## (0.196) (0.089)
##
## Acoso_sexual 0.120 -0.263*
## (0.343) (0.155)
##
## Violacion_simple 0.348 0.297**
## (0.266) (0.120)
##
## Violacion_equiparada -0.079 0.247*
## (0.275) (0.124)
##
## Tomas_Huachicol_G_GLP -0.008 -0.007*
## (0.008) (0.004)
##
## Robo_4_R_T 0.013 0.001
## (0.010) (0.005)
##
## Robo_4R_V 0.021 0.001
## (0.022) (0.010)
##
## Robo_transportista_T 0.746 0.237
## (0.562) (0.253)
##
## Robo_transportista_V -0.739 -0.229
## (0.566) (0.255)
##
## Extorsion -0.083 -0.128
## (0.437) (0.197)
##
## Despojo 0.109 0.066
## (0.134) (0.060)
##
## Narcomenudeo -0.053 -0.0002
## (0.058) (0.026)
##
## ------------------------------------------------------
## Observations 96 96
## R2 0.476 0.557
## Adjusted R2 0.257 0.372
## F Statistic (df = 21; 67) 2.896*** 4.011***
## ======================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Ajuste del modelo: Quitando Homicidio_D_A_F, Robo_4R_V y
Robo_transportista_V
modelo_fge_limpio <- plm(Desaparicion_FGE ~ Homicidio_D_T + Feminicidio_FGE +
Masacres + Ejecucion_extrajudicial + Secuestro_EEO +
Corrupcion_Trafico_M_R + Trata_T_P + Violencia_familiar. +
Abuso_sexual + Acoso_sexual + Violacion_simple +
Violacion_equiparada + Tomas_Huachicol_G_GLP +
Robo_4_R_T +
Robo_transportista_T +
Extorsion + Despojo + Narcomenudeo,
data = panel_datos,
model = "within")
modelo_cnb_limpio <- plm(Desaparicion_CNB_2025 ~ Homicidio_D_T + Feminicidio_FGE +
Masacres + Ejecucion_extrajudicial + Secuestro_EEO +
Corrupcion_Trafico_M_R + Trata_T_P + Violencia_familiar. +
Abuso_sexual + Acoso_sexual + Violacion_simple +
Violacion_equiparada + Tomas_Huachicol_G_GLP +
Robo_4_R_T +
Robo_transportista_T +
Extorsion + Despojo + Narcomenudeo,
data = panel_datos,
model = "within")
summary(modelo_fge_limpio)
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = Desaparicion_FGE ~ Homicidio_D_T + Feminicidio_FGE +
## Masacres + Ejecucion_extrajudicial + Secuestro_EEO + Corrupcion_Trafico_M_R +
## Trata_T_P + Violencia_familiar. + Abuso_sexual + Acoso_sexual +
## Violacion_simple + Violacion_equiparada + Tomas_Huachicol_G_GLP +
## Robo_4_R_T + Robo_transportista_T + Extorsion + Despojo +
## Narcomenudeo, data = panel_datos, model = "within")
##
## Balanced Panel: n = 8, T = 12, N = 96
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -20.00846 -4.85737 -0.23802 4.50128 36.30783
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## Homicidio_D_T 0.1319202 0.1444242 0.9134 0.36416
## Feminicidio_FGE -0.6021358 0.8764081 -0.6870 0.49432
## Masacres -0.6418986 0.8390136 -0.7651 0.44681
## Ejecucion_extrajudicial -1.1638225 1.0996937 -1.0583 0.29355
## Secuestro_EEO 1.1390755 1.3096467 0.8698 0.38741
## Corrupcion_Trafico_M_R -1.9986730 3.2175063 -0.6212 0.53649
## Trata_T_P -0.5868229 0.8709268 -0.6738 0.50266
## Violencia_familiar. 0.0051343 0.0336184 0.1527 0.87906
## Abuso_sexual 0.2302756 0.2023863 1.1378 0.25908
## Acoso_sexual 0.2759917 0.3551639 0.7771 0.43973
## Violacion_simple 0.5832388 0.2630915 2.2169 0.02988 *
## Violacion_equiparada -0.0470291 0.2772227 -0.1696 0.86578
## Tomas_Huachicol_G_GLP -0.0070269 0.0086065 -0.8165 0.41701
## Robo_4_R_T 0.0208395 0.0098088 2.1246 0.03716 *
## Robo_transportista_T 0.0159773 0.0114290 1.3980 0.16654
## Extorsion -0.1648925 0.4487162 -0.3675 0.71437
## Despojo 0.0894137 0.1375774 0.6499 0.51787
## Narcomenudeo -0.0746482 0.0599951 -1.2442 0.21756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 11832
## Residual Sum of Squares: 7263.2
## R-Squared: 0.38613
## Adj. R-Squared: 0.16689
## F-statistic: 2.44617 on 18 and 70 DF, p-value: 0.0040913
summary(modelo_cnb_limpio)
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = Desaparicion_CNB_2025 ~ Homicidio_D_T + Feminicidio_FGE +
## Masacres + Ejecucion_extrajudicial + Secuestro_EEO + Corrupcion_Trafico_M_R +
## Trata_T_P + Violencia_familiar. + Abuso_sexual + Acoso_sexual +
## Violacion_simple + Violacion_equiparada + Tomas_Huachicol_G_GLP +
## Robo_4_R_T + Robo_transportista_T + Extorsion + Despojo +
## Narcomenudeo, data = panel_datos, model = "within")
##
## Balanced Panel: n = 8, T = 12, N = 96
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -7.25813 -2.47251 -0.22566 1.77156 22.00807
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## Homicidio_D_T 0.1090300 0.0678863 1.6061 0.1127611
## Feminicidio_FGE 0.6247203 0.4119535 1.5165 0.1339006
## Masacres -0.9099770 0.3943763 -2.3074 0.0239954 *
## Ejecucion_extrajudicial -0.2352935 0.5169084 -0.4552 0.6503785
## Secuestro_EEO 0.5200416 0.6155963 0.8448 0.4011135
## Corrupcion_Trafico_M_R -0.8235657 1.5123811 -0.5445 0.5877943
## Trata_T_P -0.0300986 0.4093771 -0.0735 0.9415996
## Violencia_familiar. -0.0218915 0.0158022 -1.3853 0.1703460
## Abuso_sexual -0.0463368 0.0951312 -0.4871 0.6277214
## Acoso_sexual -0.1515546 0.1669439 -0.9078 0.3670896
## Violacion_simple 0.4330657 0.1236655 3.5019 0.0008088 ***
## Violacion_equiparada 0.2733846 0.1303079 2.0980 0.0395173 *
## Tomas_Huachicol_G_GLP -0.0080411 0.0040455 -1.9877 0.0507597 .
## Robo_4_R_T 0.0037696 0.0046106 0.8176 0.4163630
## Robo_transportista_T 0.0109868 0.0053722 2.0451 0.0446031 *
## Extorsion -0.1923166 0.2109180 -0.9118 0.3649995
## Despojo 0.0572685 0.0646679 0.8856 0.3788766
## Narcomenudeo -0.0131479 0.0282005 -0.4662 0.6424993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2846.5
## Residual Sum of Squares: 1604.8
## R-Squared: 0.43623
## Adj. R-Squared: 0.23488
## F-statistic: 3.0091 on 18 and 70 DF, p-value: 0.00050797
stargazer(modelo_fge_limpio, modelo_cnb_limpio,
type = "text",
title = "Contraste Final FGE vs CNB (Variables Depuradas)",
column.labels = c("Fiscalía (FGE)", "CNB (2025)"),
dep.var.labels.include = FALSE,
model.numbers = FALSE)
##
## Contraste Final FGE vs CNB (Variables Depuradas)
## ======================================================
## Dependent variable:
## ----------------------------
## Fiscalía (FGE) CNB (2025)
## ------------------------------------------------------
## Homicidio_D_T 0.132 0.109
## (0.144) (0.068)
##
## Feminicidio_FGE -0.602 0.625
## (0.876) (0.412)
##
## Masacres -0.642 -0.910**
## (0.839) (0.394)
##
## Ejecucion_extrajudicial -1.164 -0.235
## (1.100) (0.517)
##
## Secuestro_EEO 1.139 0.520
## (1.310) (0.616)
##
## Corrupcion_Trafico_M_R -1.999 -0.824
## (3.218) (1.512)
##
## Trata_T_P -0.587 -0.030
## (0.871) (0.409)
##
## Violencia_familiar. 0.005 -0.022
## (0.034) (0.016)
##
## Abuso_sexual 0.230 -0.046
## (0.202) (0.095)
##
## Acoso_sexual 0.276 -0.152
## (0.355) (0.167)
##
## Violacion_simple 0.583** 0.433***
## (0.263) (0.124)
##
## Violacion_equiparada -0.047 0.273**
## (0.277) (0.130)
##
## Tomas_Huachicol_G_GLP -0.007 -0.008*
## (0.009) (0.004)
##
## Robo_4_R_T 0.021** 0.004
## (0.010) (0.005)
##
## Robo_transportista_T 0.016 0.011**
## (0.011) (0.005)
##
## Extorsion -0.165 -0.192
## (0.449) (0.211)
##
## Despojo 0.089 0.057
## (0.138) (0.065)
##
## Narcomenudeo -0.075 -0.013
## (0.060) (0.028)
##
## ------------------------------------------------------
## Observations 96 96
## R2 0.386 0.436
## Adjusted R2 0.167 0.235
## F Statistic (df = 18; 70) 2.446*** 3.009***
## ======================================================
## Note: *p<0.1; **p<0.05; ***p<0.01