install.packages("readr")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
library(readr)
library(foreign)
link="https://docs.google.com/spreadsheets/d/e/2PACX-1vRq_z7dcVQae82F92aErK8S6f3Tv0E0hI_8tiNFRqRy3Lp4gyj0Tf8HCcBSWeboQgCquoB4D56DhOdx/pub?output=csv"
midata=read.csv(link, stringsAsFactors = T)
summary(midata)
## poblacioncienmil nbi consejocomunal priorizado
## Min. : 0.00158 Min. : 5.36 Min. :0.00000 Min. :0.0000
## 1st Qu.: 0.07422 1st Qu.:28.35 1st Qu.:0.00000 1st Qu.:0.0000
## Median : 0.13998 Median :41.30 Median :0.00000 Median :0.0000
## Mean : 0.40470 Mean :42.96 Mean :0.05474 Mean :0.2518
## 3rd Qu.: 0.26255 3rd Qu.:55.48 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :69.26836 Max. :98.81 Max. :1.00000 Max. :1.0000
## NA's :30
## uribista ejecucion apropiaciondolar pctopo
## Min. :0.0000 Min. :0.00000 Min. : 0.000 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.: 0.000 1st Qu.: 5.922
## Median :1.0000 Median :0.00000 Median : 0.000 Median :20.308
## Mean :0.6278 Mean :0.03741 Mean : 8.276 Mean :27.874
## 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.: 9.385 3rd Qu.:45.711
## Max. :1.0000 Max. :1.00000 Max. :132.643 Max. :99.419
## NA's :204 NA's :7
newOrder=c("apropiaciondolar","pctopo","uribista","priorizado","consejocomunal","ejecucion","poblacioncienmil","nbi")
midata=midata[,newOrder]
str(midata)
## 'data.frame': 1096 obs. of 8 variables:
## $ apropiaciondolar: num 102.17 4.19 1.59 0 0 ...
## $ pctopo : num 14.82 14.51 15.08 6.15 47.31 ...
## $ uribista : num 0 1 1 1 1 1 1 1 1 NA ...
## $ priorizado : num 0 1 0 0 0 0 0 0 1 0 ...
## $ consejocomunal : num 0 0 0 0 0 0 0 0 0 0 ...
## $ ejecucion : num 0 0 0 0 0 0 0 0 0 0 ...
## $ poblacioncienmil: num 20.9155 0.2406 0.0398 0.0558 0.2723 ...
## $ nbi : num 12.2 33.8 28.5 33.1 27.1 ...
install.packages("papeR")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
library(papeR)
## Loading required package: car
## Loading required package: carData
## Loading required package: xtable
## Registered S3 method overwritten by 'papeR':
## method from
## Anova.lme car
##
## Attaching package: 'papeR'
## The following object is masked from 'package:utils':
##
## toLatex
library(car)
library(carData)
library(xtable)
modelo = lm(apropiaciondolar ~ nbi, data=midata)
summary(modelo)
##
## Call:
## lm(formula = apropiaciondolar ~ nbi, data = midata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.426 -9.326 -5.943 1.937 118.822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.27938 1.22247 12.499 < 2e-16 ***
## nbi -0.15924 0.02609 -6.103 1.46e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.92 on 1064 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.03382, Adjusted R-squared: 0.03291
## F-statistic: 37.25 on 1 and 1064 DF, p-value: 1.455e-09
modelo_pavimentando = lm(apropiaciondolar ~ priorizado + pctopo + uribista + consejocomunal + ejecucion + poblacioncienmil + nbi, data=midata)
summary(modelo_pavimentando)
##
## Call:
## lm(formula = apropiaciondolar ~ priorizado + pctopo + uribista +
## consejocomunal + ejecucion + poblacioncienmil + nbi, data = midata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -60.201 -8.207 -5.875 2.505 92.488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.13182 1.67651 8.429 < 2e-16 ***
## priorizado -2.21395 1.18715 -1.865 0.06253 .
## pctopo -0.03096 0.02126 -1.456 0.14563
## uribista -2.57184 1.09188 -2.355 0.01872 *
## consejocomunal 14.05017 2.32452 6.044 2.23e-09 ***
## ejecucion 2.95688 2.80758 1.053 0.29255
## poblacioncienmil 1.83866 0.20024 9.182 < 2e-16 ***
## nbi -0.09292 0.02941 -3.160 0.00163 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.38 on 868 degrees of freedom
## (220 observations deleted due to missingness)
## Multiple R-squared: 0.1751, Adjusted R-squared: 0.1684
## F-statistic: 26.32 on 7 and 868 DF, p-value: < 2.2e-16
Regresión.Lineal<- lm(apropiaciondolar ~., data = midata)
pretty_lm <- prettify(summary(Regresión.Lineal))