str(data)
## 'data.frame': 25 obs. of 8 variables:
## $ DEPARTAMENTO : chr "AMAZONAS" "ÁNCASH" "APURÍMAC" "AREQUIPA" ...
## $ UBIGEO : chr "010000" "020000" "030000" "040000" ...
## $ buenEstado : num 18.6 13.9 8.7 27.4 17 18 33.8 11.9 10.1 15.6 ...
## $ contribuyentesSunat: num 75035 302906 103981 585628 151191 ...
## $ peaOcupada : num 130019 387976 140341 645001 235857 ...
## $ pobUrbana : num 205976 806065 243354 1383694 444473 ...
## $ PobRural : num 211389 333050 180905 76739 206467 ...
## $ pobTotal : num 417365 1139115 424259 1460433 650940 ...
el buen estado de los locales escolares depende del porcentaje de la
poblacion que contribuye a la SUNAT; y del porcentaje de la PEA que está
laborando; se llega a comprobar que (con una significancia del 0.05):
LOGISTICA BINARIA
reg_ols = lm(buenEstado ~ contribuyentesSunat + peaOcupada, data = data)
summary(reg_ols)
##
## Call:
## lm(formula = buenEstado ~ contribuyentesSunat + peaOcupada, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.589 -3.966 -1.347 1.907 21.518
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.865e+01 2.694e+00 6.922 5.98e-07 ***
## contribuyentesSunat 1.786e-05 2.060e-05 0.867 0.395
## peaOcupada -1.596e-05 2.241e-05 -0.712 0.484
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.925 on 22 degrees of freedom
## Multiple R-squared: 0.1561, Adjusted R-squared: 0.07939
## F-statistic: 2.035 on 2 and 22 DF, p-value: 0.1546
la cantidad de PEA ocupada dependen de la cantidad de contribuyentes
a la SUNAT ; y del porcentaje de locales escolares en buen estado; se
llega a comprobar que (con una significancia del 0.05): REGRE
POISSON
h1=formula(peaOcupada~contribuyentesSunat+ buenEstado )
rp1=glm(h1, data = data,
offset=log(pobTotal), #exposure
family = poisson(link = "log"))
library(modelsummary)
modelslmpoi=list('POISSON '=rp1)
modelsummary(modelslmpoi, title = "Regresion Poisson",
stars = TRUE,
output = "kableExtra")
Regresion Poisson
|
|
POISSON
|
|
(Intercept)
|
-1.159***
|
|
|
(0.001)
|
|
contribuyentesSunat
|
0.000***
|
|
|
(0.000)
|
|
buenEstado
|
0.008***
|
|
|
(0.000)
|
|
Num.Obs.
|
25
|
|
AIC
|
55974.5
|
|
BIC
|
55978.2
|
|
Log.Lik.
|
-27984.274
|
|
F
|
68713.131
|
|
RMSE
|
30706.19
|
|
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p <
0.001
|
modelsummary(modelslmpoi, title = "Regresiones Poisson",
stars = TRUE,
statistic = 'conf.int',
exponentiate = T, # exponenciar!!!!!
output = "kableExtra")
Regresiones Poisson
|
|
POISSON
|
|
(Intercept)
|
0.314***
|
|
|
[0.313, 0.315]
|
|
contribuyentesSunat
|
1.000***
|
|
|
[1.000, 1.000]
|
|
buenEstado
|
1.008***
|
|
|
[1.008, 1.008]
|
|
Num.Obs.
|
25
|
|
AIC
|
55974.5
|
|
BIC
|
55978.2
|
|
Log.Lik.
|
-27984.274
|
|
F
|
68713.131
|
|
RMSE
|
30706.19
|
|
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p <
0.001
|
library(magrittr)
overdispersion=AER::dispersiontest(rp1,alternative='greater')$ p.value<0.05
underdispersion=AER::dispersiontest(rp1,alternative='less')$ p.value<0.05
library(knitr)
testResult=as.data.frame(rbind(overdispersion,underdispersion))
names(testResult)='Es probable?'
testResult%>%kable(caption = "Test de Equidispersión")%>%kableExtra::kable_styling()
Test de Equidispersión
|
|
Es probable?
|
|
overdispersion
|
TRUE
|
|
underdispersion
|
FALSE
|
library(MASS)
rbn=glm.nb(h1,data=data)
modelsQP_BN=list('Poisson PEA ocupada)'=rp1,
'Binomial Negativa PEA ocupada (II)'=rbn)
modelsummary(modelsQP_BN,
statistic = 'conf.int',
title = "EXP() de la Regresiones Poisson y Binomial Negativa",
stars = TRUE,
output = "kableExtra")
EXP() de la Regresiones Poisson y Binomial Negativa
|
|
Poisson PEA ocupada)
|
Binomial Negativa PEA ocupada (II)
|
|
(Intercept)
|
-1.159***
|
12.297***
|
|
|
[-1.161, -1.157]
|
[11.714, 12.898]
|
|
contribuyentesSunat
|
0.000***
|
0.000***
|
|
|
[0.000, 0.000]
|
[0.000, 0.000]
|
|
buenEstado
|
0.008***
|
0.008
|
|
|
[0.008, 0.008]
|
[-0.022, 0.041]
|
|
Num.Obs.
|
25
|
25
|
|
AIC
|
55974.5
|
681.3
|
|
BIC
|
55978.2
|
686.2
|
|
Log.Lik.
|
-27984.274
|
-336.657
|
|
F
|
68713.131
|
16.323
|
|
RMSE
|
30706.19
|
568573.86
|
|
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p <
0.001
|