TALLLER_01_ECONOMETRIA
Perez_ Ruiz
2026-05-26
Librerias
library(magrittr)
library(dplyr)
library(car)
library(lmtest)
library(tseries)Carga de base de datos
library(readxl)
DTX <- read_excel("C:/Users/Mayller Perez/Desktop/UNF6/UNF 9/ECONOMETRIA/DTX.xlsx")
DTX## # A tibble: 753 × 23
## AGE CITY EDUC EXPER EXPERSQ FAMINC FATHEDUC HOURS HUSAGE HUSEDUC HUSHRS
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 32 0 12 14 196 16310 7 1610 34 12 2708
## 2 30 1 12 5 25 21800 7 1656 30 9 2310
## 3 35 0 12 15 225 21040 7 1980 40 12 3072
## 4 34 0 12 6 36 7300 7 456 53 10 1920
## 5 31 1 14 7 49 27300 14 1568 32 12 2000
## 6 54 1 12 33 1089 19495 7 2032 57 11 1040
## 7 37 0 16 11 121 21152 7 1440 37 12 2670
## 8 54 0 12 35 1225 18900 3 1020 53 8 4120
## 9 48 0 12 24 576 20405 7 1458 52 4 1995
## 10 39 0 12 21 441 20425 7 1600 43 12 2100
## # ℹ 743 more rows
## # ℹ 12 more variables: HUSWAGE <dbl>, INLF <dbl>, INLFF <dbl>, KIDSGE6 <dbl>,
## # KIDSLT6 <dbl>, LWAGE <dbl>, MOTHEDUC <dbl>, MTR <dbl>, NWIFEINC <dbl>,
## # REPWAGE <dbl>, UNEM <dbl>, WAGE <dbl>Descripción de la base de datos
attach(DTX)
glimpse(DTX)## Rows: 753
## Columns: 23
## $ AGE <dbl> 32, 30, 35, 34, 31, 54, 37, 54, 48, 39, 33, 42, 30, 43, 43, 3…
## $ CITY <dbl> 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0…
## $ EDUC <dbl> 12, 12, 12, 12, 14, 12, 16, 12, 12, 12, 12, 11, 12, 12, 10, 1…
## $ EXPER <dbl> 14, 5, 15, 6, 7, 33, 11, 35, 24, 21, 15, 14, 0, 14, 6, 9, 20,…
## $ EXPERSQ <dbl> 196, 25, 225, 36, 49, 1089, 121, 1225, 576, 441, 225, 196, 0,…
## $ FAMINC <dbl> 16310, 21800, 21040, 7300, 27300, 19495, 21152, 18900, 20405,…
## $ FATHEDUC <dbl> 7, 7, 7, 7, 14, 7, 7, 3, 7, 7, 3, 7, 16, 10, 7, 10, 7, 12, 7,…
## $ HOURS <dbl> 1610, 1656, 1980, 456, 1568, 2032, 1440, 1020, 1458, 1600, 19…
## $ HUSAGE <dbl> 34, 30, 40, 53, 32, 57, 37, 53, 52, 43, 34, 47, 33, 46, 45, 3…
## $ HUSEDUC <dbl> 12, 9, 12, 10, 12, 11, 12, 8, 4, 12, 12, 14, 16, 12, 17, 12, …
## $ HUSHRS <dbl> 2708, 2310, 3072, 1920, 2000, 1040, 2670, 4120, 1995, 2100, 2…
## $ HUSWAGE <dbl> 4.0288, 8.4416, 3.5807, 3.5417, 10.0000, 6.7106, 3.4277, 2.54…
## $ INLF <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ INLFF <dbl> 0.6636123, 0.7009166, 0.6727286, 0.7257441, 0.5616358, 0.7928…
## $ KIDSGE6 <dbl> 0, 2, 3, 3, 2, 0, 2, 0, 2, 2, 1, 1, 2, 2, 1, 3, 2, 5, 0, 4, 2…
## $ KIDSLT6 <dbl> 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ LWAGE <dbl> 1.21015370, 0.32851210, 1.51413774, 0.09212332, 1.52427220, 1…
## $ MOTHEDUC <dbl> 12, 7, 12, 7, 12, 14, 14, 3, 7, 7, 12, 14, 16, 10, 7, 16, 10,…
## $ MTR <dbl> 0.7215, 0.6615, 0.6915, 0.7815, 0.6215, 0.6915, 0.6915, 0.691…
## $ NWIFEINC <dbl> 10.910060, 19.499980, 12.039910, 6.799996, 20.100058, 9.85905…
## $ REPWAGE <dbl> 2.65, 2.65, 4.04, 3.25, 3.60, 4.70, 5.95, 9.98, 0.00, 4.15, 4…
## $ UNEM <dbl> 5.0, 11.0, 5.0, 5.0, 9.5, 7.5, 5.0, 5.0, 3.0, 5.0, 5.0, 5.0, …
## $ WAGE <dbl> 3.3540, 1.3889, 4.5455, 1.0965, 4.5918, 4.7421, 8.3333, 7.843…3.2 Estimación del Modelo de Probabilidad Lineal
model_01 <- lm(INLF ~ NWIFEINC+ EDUC+ EXPER+ I(EXPER^2)+ AGE+ KIDSLT6+ KIDSGE6, data= DTX)summary(model_01)##
## Call:
## lm(formula = INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) + AGE +
## KIDSLT6 + KIDSGE6, data = DTX)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.93432 -0.37526 0.08833 0.34404 0.99417
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5855192 0.1541780 3.798 0.000158 ***
## NWIFEINC -0.0034052 0.0014485 -2.351 0.018991 *
## EDUC 0.0379953 0.0073760 5.151 3.32e-07 ***
## EXPER 0.0394924 0.0056727 6.962 7.38e-12 ***
## I(EXPER^2) -0.0005963 0.0001848 -3.227 0.001306 **
## AGE -0.0160908 0.0024847 -6.476 1.71e-10 ***
## KIDSLT6 -0.2618105 0.0335058 -7.814 1.89e-14 ***
## KIDSGE6 0.0130122 0.0131960 0.986 0.324415
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4271 on 745 degrees of freedom
## Multiple R-squared: 0.2642, Adjusted R-squared: 0.2573
## F-statistic: 38.22 on 7 and 745 DF, p-value: < 2.2e-16Calculo las probabilidades predichas
pmpl <- predict(model_01, type= "response")
plot(pmpl, ylab= "Valores Predichos")
abline(h= 0, col= "red")
abline(h=1, col= "red")
Se observa que las probabilidades estimadas exceden el rango teórico de los valores binarios [0,1], lo que evidencia una limitación del modelo. En particular, el modelo no logra representar adecuadamente el comportamiento de las mujeres ni interpretar de forma consistente las probabilidades asociadas a su participación en el mercado laboral.
Observaciones que superan valores binarios
pmpl <- as.data.frame(pmpl)
pmpl %>% filter(pmpl < 0 | pmpl > 1)## pmpl
## 26 1.038794494
## 35 1.082995379
## 40 1.051385788
## 78 1.018047165
## 142 1.014867288
## 143 1.035768808
## 150 1.033742713
## 158 1.049001043
## 163 1.072299986
## 167 1.024547068
## 280 1.037039738
## 300 1.127150535
## 304 1.001785732
## 332 1.016913659
## 345 1.035426409
## 392 1.036876856
## 398 1.002709062
## 484 -0.329286062
## 485 -0.003253122
## 487 -0.028129715
## 514 -0.037761684
## 531 -0.345110267
## 562 -0.114702093
## 586 -0.163268978
## 593 -0.025559153
## 596 -0.152886466
## 605 -0.159633260
## 621 -0.012844175
## 640 -0.013146299
## 650 -0.085643778
## 677 -0.172159973
## 689 -0.102660570
## 715 -0.120588118head(pmpl %>% filter(pmpl < 0 | pmpl > 1), n= 10)## pmpl
## 26 1.038794
## 35 1.082995
## 40 1.051386
## 78 1.018047
## 142 1.014867
## 143 1.035769
## 150 1.033743
## 158 1.049001
## 163 1.072300
## 167 1.024547Las observaciones que superan los valores binarios son 33
p <- pmpl %>% filter(pmpl < 0 | pmpl > 1)
p %>% mutate(d= 1) %>% summarise( sum(d))## sum(d)
## 1 33Grafique las probabilidades estimadas
plot(pmpl$pmpl, DTX$INLF, ylab = "Linea de predicción y estimado", xlab= "Participación del Mercado Laboral", type = "p", pch= 16)
abline(lm(DTX$INLF ~ pmpl$pmpl), col= "red", lwd= 2, lty =2)
Contraste heterocedasticidad
Prueba gráfica
Se tiene como media de los errores:
mean(residuals(model_01))## [1] 1.098275e-16En la figura se observa que los errores se distribuyen de manera desigual alrededor de cero, lo que sugiere la presencia de indicios de heterocedasticidad.
plot(residuals(model_01), type = "l", ylab = "residuos")
abline(h= 0, col= "red")
legend("bottomleft", legend = c("media= 1.098275e-16"), cex = 0.8, bty = "n")
Prueba Breusch-Pagan (BP)
Hay evidencia estadísticamente significativa de heterocedasticidad en el modelo. Al
bptest(model_01)##
## studentized Breusch-Pagan test
##
## data: model_01
## BP = 24.224, df = 7, p-value = 0.00104Contraste normalidad de residuos
Gráfica
hist(residuals(model_01), freq = FALSE, main= " ", xlab = "Residuos", ylab = "Densidad")
curve(dnorm(x,mean= mean(residuals(model_01)), sd= sd(residuals(model_01))), add = TRUE, col= "red", lwd= 2)
Estadístico Jarque-Bera
jarque.bera.test(model_01$residuals)##
## Jarque Bera Test
##
## data: model_01$residuals
## X-squared = 36.741, df = 2, p-value = 1.051e-083.3. Estimación del Modelo Probit
model_02 <- glm(INLF ~ NWIFEINC+ EDUC+ EXPER+ I(EXPER^2)+ AGE+ KIDSLT6+ KIDSGE6, family = binomial(link = "probit"))
summary(model_02)##
## Call:
## glm(formula = INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) + AGE +
## KIDSLT6 + KIDSGE6, family = binomial(link = "probit"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2700736 0.5080782 0.532 0.59503
## NWIFEINC -0.0120236 0.0049392 -2.434 0.01492 *
## EDUC 0.1309040 0.0253987 5.154 2.55e-07 ***
## EXPER 0.1233472 0.0187587 6.575 4.85e-11 ***
## I(EXPER^2) -0.0018871 0.0005999 -3.145 0.00166 **
## AGE -0.0528524 0.0084624 -6.246 4.22e-10 ***
## KIDSLT6 -0.8683247 0.1183773 -7.335 2.21e-13 ***
## KIDSGE6 0.0360056 0.0440303 0.818 0.41350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1029.7 on 752 degrees of freedom
## Residual deviance: 802.6 on 745 degrees of freedom
## AIC: 818.6
##
## Number of Fisher Scoring iterations: 4Analice la significancia global mediante razón de verosimilitud
Modelo restringido
modelo_null <- glm(INLF ~ 1, family = binomial(link = "probit"), data = DTX)anova(modelo_null, model_02, test = "Chisq")## Analysis of Deviance Table
##
## Model 1: INLF ~ 1
## Model 2: INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) + AGE + KIDSLT6 +
## KIDSGE6
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 752 1029.8
## 2 745 802.6 7 227.14 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1log-likelihood
Modelo completo
logLik(model_02)## 'log Lik.' -401.3022 (df=8)Modelo restringido
logLik(modelo_null)## 'log Lik.' -514.8732 (df=1)Pseudo R2 de McFadden
library(pscl)
pR2(model_02)## fitting null model for pseudo-r2## llh llhNull G2 McFadden r2ML r2CU
## -401.3021931 -514.8732046 227.1420229 0.2205805 0.2604027 0.3494103Calcule AIC y BIC
AIC y BIC - modelo completo
AIC(model_02)## [1] 818.6044BIC(model_02)## [1] 855.5969AIC y BIC - modelo restringido
AIC(modelo_null)## [1] 1031.746BIC(modelo_null)## [1] 1036.37Obtenga las probabilidades predichas
Probabilidades predichas
pprobit <- predict(model_02, type= "response")
pprobit <- as.data.frame(pprobit)
pprobit## pprobit
## 1 0.693969922
## 2 0.746161015
## 3 0.695545613
## 4 0.771060201
## 5 0.578195028
## 6 0.811594602
## 7 0.933121631
## 8 0.805470330
## 9 0.851138180
## 10 0.927078882
## 11 0.897286372
## 12 0.740973293
## 13 0.235356281
## 14 0.794383987
## 15 0.389147852
## 16 0.762597822
## 17 0.881567834
## 18 0.670541979
## 19 0.871131112
## 20 0.833611935
## 21 0.696342413
## 22 0.900876125
## 23 0.797742838
## 24 0.780307662
## 25 0.395072242
## 26 0.957650119
## 27 0.705587431
## 28 0.843879361
## 29 0.718400670
## 30 0.580554263
## 31 0.846277754
## 32 0.568804234
## 33 0.928960555
## 34 0.630373468
## 35 0.967676491
## 36 0.259632433
## 37 0.798749936
## 38 0.846441004
## 39 0.865455278
## 40 0.966220163
## 41 0.272260042
## 42 0.468439732
## 43 0.629834359
## 44 0.571572700
## 45 0.806650536
## 46 0.698128925
## 47 0.754914611
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## 84 0.314958795
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## 93 0.641533594
## 94 0.383265863
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## 749 0.563650569
## 750 0.424898182
## 751 0.464859880
## 752 0.418783523
## 753 0.641466999Gráfico
plot(pprobit$pprobit, ylab= "Valores Predichos", xlab= "Observaciones")
abline(h= 0, col= "red")
abline(h=1, col= "red")
Calcule los efectos marginales
Efectos marginales en la media - MEM
library(mfx)## Warning: package 'mfx' was built under R version 4.4.3## Cargando paquete requerido: sandwich## Warning: package 'sandwich' was built under R version 4.4.1## Cargando paquete requerido: MASS## Warning: package 'MASS' was built under R version 4.4.2##
## Adjuntando el paquete: 'MASS'## The following object is masked from 'package:dplyr':
##
## select## Cargando paquete requerido: betareg## Warning: package 'betareg' was built under R version 4.4.3MEMP <- probitmfx(INLF ~ NWIFEINC+ EDUC+ EXPER+ I(EXPER^2)+ AGE+ KIDSLT6+ KIDSGE6, data = DTX, atmean = TRUE)
MEMP## Call:
## probitmfx(formula = INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) +
## AGE + KIDSLT6 + KIDSGE6, data = DTX, atmean = TRUE)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## NWIFEINC -0.00469619 0.00192965 -2.4337 0.014945 *
## EDUC 0.05112843 0.00992310 5.1525 2.571e-07 ***
## EXPER 0.04817690 0.00734505 6.5591 5.413e-11 ***
## I(EXPER^2) -0.00073705 0.00023464 -3.1412 0.001683 **
## AGE -0.02064309 0.00330485 -6.2463 4.203e-10 ***
## KIDSLT6 -0.33914996 0.04634765 -7.3175 2.526e-13 ***
## KIDSGE6 0.01406306 0.01719895 0.8177 0.413546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Efectos marginales promedio AME
library(mfx)
AMEP <- probitmfx(INLF ~ NWIFEINC+ EDUC+ EXPER+ I(EXPER^2)+ AGE+ KIDSLT6+ KIDSGE6, data = DTX, atmean = FALSE)
AMEP## Call:
## probitmfx(formula = INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) +
## AGE + KIDSLT6 + KIDSGE6, data = DTX, atmean = FALSE)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## NWIFEINC -0.00361618 0.00146972 -2.4604 0.013876 *
## EDUC 0.03937009 0.00726571 5.4186 6.006e-08 ***
## EXPER 0.03709734 0.00516823 7.1780 7.076e-13 ***
## I(EXPER^2) -0.00056755 0.00017708 -3.2050 0.001351 **
## AGE -0.01589566 0.00235868 -6.7392 1.592e-11 ***
## KIDSLT6 -0.26115346 0.03190239 -8.1860 2.700e-16 ***
## KIDSGE6 0.01082889 0.01322413 0.8189 0.412859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.4. Estimación del Modelo Logit
model_03 <- glm(INLF ~ NWIFEINC+ EDUC+ EXPER+ I(EXPER^2)+ AGE+ KIDSLT6+ KIDSGE6, family = binomial(link = "logit"))
summary(model_03)##
## Call:
## glm(formula = INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) + AGE +
## KIDSLT6 + KIDSGE6, family = binomial(link = "logit"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.425452 0.860365 0.495 0.62095
## NWIFEINC -0.021345 0.008421 -2.535 0.01126 *
## EDUC 0.221170 0.043439 5.091 3.55e-07 ***
## EXPER 0.205870 0.032057 6.422 1.34e-10 ***
## I(EXPER^2) -0.003154 0.001016 -3.104 0.00191 **
## AGE -0.088024 0.014573 -6.040 1.54e-09 ***
## KIDSLT6 -1.443354 0.203583 -7.090 1.34e-12 ***
## KIDSGE6 0.060112 0.074789 0.804 0.42154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1029.75 on 752 degrees of freedom
## Residual deviance: 803.53 on 745 degrees of freedom
## AIC: 819.53
##
## Number of Fisher Scoring iterations: 4ODDS- RATIO
exp(model_03$coefficients)## (Intercept) NWIFEINC EDUC EXPER I(EXPER^2) AGE
## 1.5302825 0.9788810 1.2475360 1.2285929 0.9968509 0.9157386
## KIDSLT6 KIDSGE6
## 0.2361344 1.0619557Analice la significancia global mediante razón de verosimilitud
model_null <- glm(INLF ~ 1, family = binomial(link = "logit"), data = DTX)
anova(model_null, model_03, test = "Chisq")## Analysis of Deviance Table
##
## Model 1: INLF ~ 1
## Model 2: INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) + AGE + KIDSLT6 +
## KIDSGE6
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 752 1029.75
## 2 745 803.53 7 226.22 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1log-likelihood
Modelo completo
logLik(model_03)## 'log Lik.' -401.7652 (df=8)Modelo restringido
logLik(model_null)## 'log Lik.' -514.8732 (df=1)Pseudo R2 de McFadden
pR2(model_03)## fitting null model for pseudo-r2## llh llhNull G2 McFadden r2ML r2CU
## -401.7651511 -514.8732046 226.2161070 0.2196814 0.2594927 0.3481893Calcule AIC y BIC
AIC y BIC - modelo completo
AIC(model_03)## [1] 819.5303BIC(model_03)## [1] 856.5228AIC y BIC - modelo restringido
AIC(model_null)## [1] 1031.746BIC(model_null)## [1] 1036.37Obtenga y grafique las probabilidades predichas
Tabla
plogit <- predict(model_03, type= "response")
plogit <- as.data.frame(plogit)
plogit## plogit
## 1 0.700662496
## 2 0.748994088
## 3 0.702033866
## 4 0.776177358
## 5 0.581138459
## 6 0.813757468
## 7 0.925191351
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## 713 0.291104794
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## 721 0.638347621
## 722 0.224229964
## 723 0.651885749
## 724 0.881818388
## 725 0.180267620
## 726 0.249954549
## 727 0.270502659
## 728 0.366409629
## 729 0.232427004
## 730 0.384913969
## 731 0.479630281
## 732 0.248080503
## 733 0.772902723
## 734 0.878976291
## 735 0.415080642
## 736 0.447775242
## 737 0.750394386
## 738 0.724178245
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## 741 0.651446489
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## 743 0.381855882
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## 745 0.696241482
## 746 0.109453077
## 747 0.222911718
## 748 0.665986159
## 749 0.561801795
## 750 0.424308018
## 751 0.463821833
## 752 0.411714085
## 753 0.639732191Gráfico
plot(plogit$plogit, ylab= "Probabilidades predichas", xlab= "Observaciones")
abline(h=0, col= "red", lwd= 2)
abline(h=1, col= "red", lwd= 2)
Efectos marginales en la media, MEM
library(mfx)
MEML <- logitmfx(INLF ~ NWIFEINC+ EDUC+ EXPER+ I(EXPER^2)+ AGE+ KIDSLT6+ KIDSGE6, data = DTX, atmean = TRUE)
MEML## Call:
## logitmfx(formula = INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) +
## AGE + KIDSLT6 + KIDSGE6, data = DTX, atmean = TRUE)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## NWIFEINC -0.00519005 0.00204820 -2.5340 0.011278 *
## EDUC 0.05377731 0.01056074 5.0922 3.539e-07 ***
## EXPER 0.05005693 0.00782462 6.3974 1.581e-10 ***
## I(EXPER^2) -0.00076692 0.00024768 -3.0965 0.001959 **
## AGE -0.02140302 0.00353973 -6.0465 1.480e-09 ***
## KIDSLT6 -0.35094982 0.04963897 -7.0700 1.549e-12 ***
## KIDSGE6 0.01461621 0.01818832 0.8036 0.421625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Efectos marginales promedio, AME
library(mfx)
AMEL <- logitmfx(INLF ~ NWIFEINC+ EDUC+ EXPER+ I(EXPER^2)+ AGE+ KIDSLT6+ KIDSGE6, data = DTX, atmean = FALSE)
AMEL## Call:
## logitmfx(formula = INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) +
## AGE + KIDSLT6 + KIDSGE6, data = DTX, atmean = FALSE)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## NWIFEINC -0.00381181 0.00153898 -2.4769 0.013255 *
## EDUC 0.03949652 0.00846811 4.6641 3.099e-06 ***
## EXPER 0.03676411 0.00655577 5.6079 2.048e-08 ***
## I(EXPER^2) -0.00056326 0.00018795 -2.9968 0.002728 **
## AGE -0.01571936 0.00293269 -5.3600 8.320e-08 ***
## KIDSLT6 -0.25775366 0.04263493 -6.0456 1.489e-09 ***
## KIDSGE6 0.01073482 0.01339130 0.8016 0.422769
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.5. Comparación de modelos
compareCoefs(model_01, model_02, model_03, print = TRUE, se= FALSE, pvals = TRUE)## Calls:
## 1: lm(formula = INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) + AGE + KIDSLT6
## + KIDSGE6, data = DTX)
## 2: glm(formula = INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) + AGE +
## KIDSLT6 + KIDSGE6, family = binomial(link = "probit"))
## 3: glm(formula = INLF ~ NWIFEINC + EDUC + EXPER + I(EXPER^2) + AGE +
## KIDSLT6 + KIDSGE6, family = binomial(link = "logit"))
##
## Model 1 Model 2 Model 3
## (Intercept) 0.586 0.270 0.425
## Pr(>|z|) 0.00015 0.59503 0.62095
##
## NWIFEINC -0.00341 -0.01202 -0.02135
## Pr(>|z|) 0.01873 0.01492 0.01126
##
## EDUC 0.038 0.131 0.221
## Pr(>|z|) 2.6e-07 2.6e-07 3.6e-07
##
## EXPER 0.0395 0.1233 0.2059
## Pr(>|z|) 3.4e-12 4.8e-11 1.3e-10
##
## I(EXPER^2) -0.000596 -0.001887 -0.003154
## Pr(>|z|) 0.00125 0.00166 0.00191
##
## AGE -0.0161 -0.0529 -0.0880
## Pr(>|z|) 9.4e-11 4.2e-10 1.5e-09
##
## KIDSLT6 -0.262 -0.868 -1.443
## Pr(>|z|) 5.5e-15 2.2e-13 1.3e-12
##
## KIDSGE6 0.0130 0.0360 0.0601
## Pr(>|z|) 0.32410 0.41350 0.42154
## Log-likelihood y Pseudo R2
print(c(logLik(model_01),logLik(model_02), logLik(model_03)))## [1] -423.8923 -401.3022 -401.7652AIC y BIC
print(c(BIC(model_01),BIC(model_02), BIC(model_03)))## [1] 907.4013 855.5969 856.5228print(c(AIC(model_01),AIC(model_02), AIC(model_03)))## [1] 865.7847 818.6044 819.5303Porcentaje de predicción correcta
MPL
mm <- pmpl %>% mutate( pmpl1 = round(pmpl,0), pml = DTX$INLF, dif= pmpl1==pml)
prop.table(table(mm$dif))##
## FALSE TRUE
## 0.2656042 0.7343958Probit
pp <- pprobit %>% mutate( pprobit1 = round(pprobit,0), pml = DTX$INLF, dif= pprobit1==pml)
prop.table(table(pp$dif))##
## FALSE TRUE
## 0.2656042 0.7343958logit
ll <- plogit %>% mutate( plogit1 = round(plogit,0), pml = DTX$INLF, dif= plogit1==pml)
prop.table(table(ll$dif))##
## FALSE TRUE
## 0.2642762 0.7357238