link="https://docs.google.com/spreadsheets/d/e/2PACX-1vTqb8BIAywRYI2aAbtlR1fzojEjP7b0VEa9II1FGNp5w2zAp1ZMfFWb760ryovcn6WlUjUVf8Y0k2b8/pub?gid=2057947663&single=true&output=csv"
data=read.csv(link, stringsAsFactors = F)str(data)## 'data.frame': 1096 obs. of 8 variables:
## $ poblacioncienmil: num 20.9155 0.2406 0.0398 0.0558 0.2723 ...
## $ nbi : num 12.2 33.8 28.5 33.1 27.1 ...
## $ consejocomunal : num 0 0 0 0 0 0 0 0 0 0 ...
## $ priorizado : num 0 1 0 0 0 0 0 0 1 0 ...
## $ uribista : num 0 1 1 1 1 1 1 1 1 NA ...
## $ ejecucion : num 0 0 0 0 0 0 0 0 0 0 ...
## $ apropiaciondolar: num 102.17 4.19 1.59 0 0 ...
## $ pctopo : num 14.82 14.51 15.08 6.15 47.31 ...data[,c(3:6)]=lapply(data[,c(3:6)],as.factor)str(data)## 'data.frame': 1096 obs. of 8 variables:
## $ poblacioncienmil: num 20.9155 0.2406 0.0398 0.0558 0.2723 ...
## $ nbi : num 12.2 33.8 28.5 33.1 27.1 ...
## $ consejocomunal : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ priorizado : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 2 1 ...
## $ uribista : Factor w/ 2 levels "0","1": 1 2 2 2 2 2 2 2 2 NA ...
## $ ejecucion : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ apropiaciondolar: num 102.17 4.19 1.59 0 0 ...
## $ pctopo : num 14.82 14.51 15.08 6.15 47.31 ...summary(data)## poblacioncienmil nbi consejocomunal priorizado uribista
## Min. : 0.00158 Min. : 5.36 0:1036 0:820 0 :332
## 1st Qu.: 0.07422 1st Qu.:28.35 1: 60 1:276 1 :560
## Median : 0.13998 Median :41.30 NA's:204
## Mean : 0.40470 Mean :42.96
## 3rd Qu.: 0.26255 3rd Qu.:55.48
## Max. :69.26836 Max. :98.81
## NA's :30
## ejecucion apropiaciondolar pctopo
## 0:1055 Min. : 0.000 Min. : 0.000
## 1: 41 1st Qu.: 0.000 1st Qu.: 5.922
## Median : 0.000 Median :20.308
## Mean : 8.276 Mean :27.874
## 3rd Qu.: 9.385 3rd Qu.:45.711
## Max. :132.643 Max. :99.419
## NA's :7newOrder=c("apropiaciondolar","pctopo","uribista","priorizado","consejocomunal","ejecucion","poblacioncienmil","nbi")
data=data[,newOrder]data$priorizado = factor(data$priorizado, labels=c("No", "Si"))
data$uribista = factor(data$uribista, labels=c("No", "Si"))
data$consejocomunal = factor(data$consejocomunal, labels=c("No", "Si"))
data$ejecucion= factor(data$ejecucion, labels=c("No", "Si"))#REGRESIÓN
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':
##
## toLatexRegr=lm(apropiaciondolar~.,data=data)
summary(Regr)##
## Call:
## lm(formula = apropiaciondolar ~ ., data = data)
##
## 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 ***
## pctopo -0.03096 0.02126 -1.456 0.14563
## uribistaSi -2.57184 1.09188 -2.355 0.01872 *
## priorizadoSi -2.21395 1.18715 -1.865 0.06253 .
## consejocomunalSi 14.05017 2.32452 6.044 2.23e-09 ***
## ejecucionSi 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-16regresión = lm(apropiaciondolar ~ priorizado + pctopo + uribista + consejocomunal + ejecucion + poblacioncienmil + nbi, data = data)
(pretty_lm <- prettify(summary(regresión)))## Estimate CI (lower) CI (upper) Std. Error t value
## 1 (Intercept) 14.13181597 10.84132194 17.42230999 1.67651334 8.429289
## 2 priorizado: Si -2.21394937 -4.54397814 0.11607941 1.18715436 -1.864921
## 3 pctopo -0.03096414 -0.07269121 0.01076292 0.02126002 -1.456449
## 4 uribista: Si -2.57183741 -4.71488038 -0.42879444 1.09188472 -2.355411
## 5 consejocomunal: Si 14.05016737 9.48782492 18.61250983 2.32452268 6.044324
## 6 ejecucion: Si 2.95687526 -2.55355452 8.46730504 2.80757509 1.053178
## 7 poblacioncienmil 1.83866214 1.44564529 2.23167898 0.20024287 9.182160
## 8 nbi -0.09292085 -0.15063803 -0.03520367 0.02940702 -3.159819
## Pr(>|t|)
## 1 <0.001 ***
## 2 0.063 .
## 3 0.146
## 4 0.019 *
## 5 <0.001 ***
## 6 0.293
## 7 <0.001 ***
## 8 0.002 **xtable(pretty_lm)## % latex table generated in R 4.0.0 by xtable 1.8-4 package
## % Mon Jul 6 01:47:05 2020
## \begin{table}[ht]
## \centering
## \begin{tabular}{rlrrrrrll}
## \hline
## & & Estimate & CI (lower) & CI (upper) & Std. Error & t value & Pr($>$$|$t$|$) & \\
## \hline
## 1 & (Intercept) & 14.13 & 10.84 & 17.42 & 1.68 & 8.43 & $<$0.001 & *** \\
## 2 & priorizado: Si & -2.21 & -4.54 & 0.12 & 1.19 & -1.86 & 0.063 & . \\
## 3 & pctopo & -0.03 & -0.07 & 0.01 & 0.02 & -1.46 & 0.146 & \\
## 4 & uribista: Si & -2.57 & -4.71 & -0.43 & 1.09 & -2.36 & 0.019 & * \\
## 5 & consejocomunal: Si & 14.05 & 9.49 & 18.61 & 2.32 & 6.04 & $<$0.001 & *** \\
## 6 & ejecucion: Si & 2.96 & -2.55 & 8.47 & 2.81 & 1.05 & 0.293 & \\
## 7 & poblacioncienmil & 1.84 & 1.45 & 2.23 & 0.20 & 9.18 & $<$0.001 & *** \\
## 8 & nbi & -0.09 & -0.15 & -0.04 & 0.03 & -3.16 & 0.002 & ** \\
## \hline
## \end{tabular}
## \end{table}library(knitr)
kable(pretty_lm)| Estimate | CI (lower) | CI (upper) | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|---|---|
| (Intercept) | 14.1318160 | 10.8413219 | 17.4223100 | 1.6765133 | 8.429289 | <0.001 | *** |
| priorizado: Si | -2.2139494 | -4.5439781 | 0.1160794 | 1.1871544 | -1.864921 | 0.063 | . |
| pctopo | -0.0309641 | -0.0726912 | 0.0107629 | 0.0212600 | -1.456449 | 0.146 | |
| uribista: Si | -2.5718374 | -4.7148804 | -0.4287944 | 1.0918847 | -2.355411 | 0.019 | * |
| consejocomunal: Si | 14.0501674 | 9.4878249 | 18.6125098 | 2.3245227 | 6.044324 | <0.001 | *** |
| ejecucion: Si | 2.9568753 | -2.5535545 | 8.4673050 | 2.8075751 | 1.053178 | 0.293 | |
| poblacioncienmil | 1.8386621 | 1.4456453 | 2.2316790 | 0.2002429 | 9.182160 | <0.001 | *** |
| nbi | -0.0929208 | -0.1506380 | -0.0352037 | 0.0294070 | -3.159819 | 0.002 | ** |