pu <- read_excel("C:/Users/Jhon/Documents/pu.xlsx")
summary(pu)## provincia numero unidad sierra
## Length:1186 Min. : 1.0 Min. : 1.000 Min. :0.0000
## Class :character 1st Qu.: 30.0 1st Qu.: 4.000 1st Qu.:1.0000
## Mode :character Median : 59.0 Median : 7.000 Median :1.0000
## Mean : 68.6 Mean : 7.448 Mean :0.7934
## 3rd Qu.: 92.0 3rd Qu.:11.000 3rd Qu.:1.0000
## Max. :1037.0 Max. :14.000 Max. :1.0000
## credito ahorro cultivo cosecha
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.: 0.200
## Median :0.0000 Median :0.0000 Median :1.000 Median : 0.550
## Mean :0.2378 Mean :0.2133 Mean :0.688 Mean : 1.472
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:1.000 3rd Qu.: 1.500
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :150.000
## produccion venta sexo edad
## Min. : 0.1 Min. : 0.0 Min. :0.0000 Min. : 9.00
## 1st Qu.: 45.0 1st Qu.: 157.8 1st Qu.:0.0000 1st Qu.:28.00
## Median : 110.5 Median : 1474.5 Median :1.0000 Median :43.00
## Mean : 399.9 Mean : 6183.5 Mean :0.5295 Mean :44.64
## 3rd Qu.: 240.0 3rd Qu.: 5383.5 3rd Qu.:1.0000 3rd Qu.:60.00
## Max. :80071.0 Max. :178045.0 Max. :1.0000 Max. :90.00
## educ
## Min. : 0.000
## 1st Qu.: 2.000
## Median : 4.000
## Mean : 4.336
## 3rd Qu.: 5.000
## Max. :10.000pu1<-select(pu,-numero,-unidad)
summary(pu1)## provincia sierra credito ahorro
## Length:1186 Min. :0.0000 Min. :0.0000 Min. :0.0000
## Class :character 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Mode :character Median :1.0000 Median :0.0000 Median :0.0000
## Mean :0.7934 Mean :0.2378 Mean :0.2133
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## cultivo cosecha produccion venta
## Min. :0.000 Min. : 0.000 Min. : 0.1 Min. : 0.0
## 1st Qu.:0.000 1st Qu.: 0.200 1st Qu.: 45.0 1st Qu.: 157.8
## Median :1.000 Median : 0.550 Median : 110.5 Median : 1474.5
## Mean :0.688 Mean : 1.472 Mean : 399.9 Mean : 6183.5
## 3rd Qu.:1.000 3rd Qu.: 1.500 3rd Qu.: 240.0 3rd Qu.: 5383.5
## Max. :1.000 Max. :150.000 Max. :80071.0 Max. :178045.0
## sexo edad educ
## Min. :0.0000 Min. : 9.00 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.:28.00 1st Qu.: 2.000
## Median :1.0000 Median :43.00 Median : 4.000
## Mean :0.5295 Mean :44.64 Mean : 4.336
## 3rd Qu.:1.0000 3rd Qu.:60.00 3rd Qu.: 5.000
## Max. :1.0000 Max. :90.00 Max. :10.000El modelo general
logit <- glm(credito ~ scale(produccion) + sierra + ahorro + cultivo +
cosecha + venta + sexo + edad + educ,
family = binomial(link = "logit"),
data = pu1)
coeftest(logit,vcov.=vcovHC, type = "HC1")##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.4297e+00 3.1342e-01 -7.7522 9.028e-15 ***
## scale(produccion) 1.0464e-02 6.8558e-02 0.1526 0.87869
## sierra -1.2319e-01 2.4039e-01 -0.5125 0.60833
## ahorro 3.9776e-01 1.8918e-01 2.1025 0.03551 *
## cultivo 8.2471e-02 1.9837e-01 0.4157 0.67760
## cosecha 1.4720e-02 1.2489e-02 1.1786 0.23855
## venta 6.6272e-05 1.3416e-05 4.9399 7.815e-07 ***
## sexo 7.9804e-02 1.5550e-01 0.5132 0.60780
## edad -4.7516e-03 4.0943e-03 -1.1605 0.24583
## educ 1.9687e-01 3.0469e-02 6.4614 1.037e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pu2 <- select(pu, provincia, credito, ahorro, venta, educ)
summary(pu2)## provincia credito ahorro venta
## Length:1186 Min. :0.0000 Min. :0.0000 Min. : 0.0
## Class :character 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 157.8
## Mode :character Median :0.0000 Median :0.0000 Median : 1474.5
## Mean :0.2378 Mean :0.2133 Mean : 6183.5
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 5383.5
## Max. :1.0000 Max. :1.0000 Max. :178045.0
## educ
## Min. : 0.000
## 1st Qu.: 2.000
## Median : 4.000
## Mean : 4.336
## 3rd Qu.: 5.000
## Max. :10.000modelo con variables significativas
logit <- glm(credito ~ ahorro + venta + educ,
family = binomial(link = "logit"),
data = pu1)
coeftest(logit,vcov.=vcovHC, type = "HC1")##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.6577e+00 1.7853e-01 -14.8862 < 2.2e-16 ***
## ahorro 3.6977e-01 1.8693e-01 1.9780 0.04792 *
## venta 7.0585e-05 1.2949e-05 5.4512 5.003e-08 ***
## educ 2.0415e-01 2.9097e-02 7.0161 2.282e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modelo con provincias Yauli
yauli<-subset(pu1, provincia == "YAULI")
logit <- glm(credito ~ ahorro + venta + educ,
family = binomial(link = "logit"),
data = yauli)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredcoeftest(logit,vcov.=vcovHC, type = "HC1")##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.3175e+01 2.1416e+00 -10.8213 < 2.2e-16 ***
## venta 1.8177e-02 5.6579e-04 32.1263 < 2.2e-16 ***
## educ -1.9582e+00 5.7525e-01 -3.4041 0.0006638 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Junin
junin<-subset(pu1, provincia == "JUNIN")
logit <- glm(credito ~ ahorro + venta + educ,
family = binomial(link = "logit"),
data = junin)
coeftest(logit,vcov.=vcovHC, type = "HC1")##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.1719e+00 1.3469e+00 -3.0973 0.001953 **
## ahorro 2.3929e+00 8.4524e-01 2.8311 0.004639 **
## venta 2.7513e-04 9.8506e-05 2.7930 0.005222 **
## educ 2.4267e-01 2.6435e-01 0.9180 0.358624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Tarma
tarma<-subset(pu1, provincia == "TARMA")
logit <- glm(credito ~ ahorro + venta + educ,
family = binomial(link = "logit"),
data = tarma)
coeftest(logit,vcov.=vcovHC, type = "HC1")##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.87783393 0.50018121 -5.7536 8.737e-09 ***
## ahorro 1.24186590 0.61182854 2.0298 0.0423808 *
## venta 0.00018876 0.00005670 3.3290 0.0008716 ***
## educ 0.21244398 0.10612199 2.0019 0.0452971 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Chanchamayo
chanchamayo<-subset(pu1, provincia == "CHANCHAMAYO")
logit <- glm(credito ~ ahorro + venta + educ,
family = binomial(link = "logit"),
data = chanchamayo)
coeftest(logit,vcov.=vcovHC, type = "HC1")##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5757e+00 4.3297e-01 -3.6393 0.0002733 ***
## ahorro 5.2339e-01 6.1375e-01 0.8528 0.3937909
## venta 4.8879e-05 1.7987e-05 2.7175 0.0065770 **
## educ 1.3947e-01 8.6616e-02 1.6103 0.1073396
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Satipo
satipo<-subset(pu1, provincia == "SATIPO")
logit <- glm(credito ~ ahorro + venta + educ,
family = binomial(link = "logit"),
data = satipo)
coeftest(logit,vcov.=vcovHC, type = "HC1")##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.1674e+00 5.0044e-01 -4.3310 1.485e-05 ***
## ahorro -2.4933e-01 6.9778e-01 -0.3573 0.72085
## venta 2.4481e-05 1.8845e-05 1.2990 0.19393
## educ 1.9065e-01 9.5098e-02 2.0048 0.04498 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Jauja
jauja<-subset(pu1, provincia == "JAUJA")
logit <- glm(credito ~ ahorro + venta + educ,
family = binomial(link = "logit"),
data = jauja)
coeftest(logit,vcov.=vcovHC, type = "HC1")##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.2780e+00 4.4983e-01 -7.2872 3.166e-13 ***
## ahorro -4.6790e-01 6.2015e-01 -0.7545 0.4505483
## venta 9.0780e-05 3.6416e-05 2.4928 0.0126734 *
## educ 2.4815e-01 6.6556e-02 3.7284 0.0001927 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Concepcion
concepcion<-subset(pu1, provincia == "CONCEPCION")
logit <- glm(credito ~ ahorro + venta + educ,
family = binomial(link = "logit"),
data = concepcion)
coeftest(logit,vcov.=vcovHC, type = "HC1")##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.7158e+00 8.6542e-01 -5.4492 5.06e-08 ***
## ahorro 1.8318e+00 6.4364e-01 2.8460 0.0044278 **
## venta 8.4576e-05 2.9433e-05 2.8735 0.0040594 **
## educ 4.8284e-01 1.3285e-01 3.6346 0.0002784 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Chupaca
chupaca<-subset(pu1, provincia == "CHUPACA")
logit <- glm(credito ~ ahorro + venta + educ,
family = binomial(link = "logit"),
data = chupaca)
coeftest(logit,vcov.=vcovHC, type = "HC1")##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.1483e+00 5.6370e-01 -5.5852 2.335e-08 ***
## ahorro -3.1772e-01 6.7708e-01 -0.4693 0.63889
## venta 4.7401e-05 2.9759e-05 1.5928 0.11119
## educ 2.3249e-01 9.1448e-02 2.5424 0.01101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Huancayo
huancayo<-subset(pu1, provincia == "HUANCAYO")
logit <- glm(credito ~ ahorro + venta + educ,
family = binomial(link = "logit"),
data = huancayo)
coeftest(logit,vcov.=vcovHC, type = "HC1")##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.1207e+00 3.8685e-01 -8.0668 7.214e-16 ***
## ahorro 6.4097e-01 4.0685e-01 1.5754 0.11516
## venta 9.4844e-05 4.8465e-05 1.9570 0.05035 .
## educ 2.6768e-01 5.9360e-02 4.5094 6.500e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1