Untitled
2023-10-13
#PREGUNTA 1 - MORTALIDAD MATERNA
library(rio)
lkXLSX="https://docs.google.com/spreadsheets/d/1s2Ne9uQ7nwTvSo6ckNQHV35NZt-OYs8V/edit#gid=1446884572"
data1=import(lkXLSX)library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdata1 <- data1 %>% filter(!is.na(TasaFertil1mMuje) & TasaFertil1mMuje != "")library(tidyverse)
data1 <- data1 %>% filter(!is.na(EmployPerPop) & EmployPerPop != "")library(tidyverse)
data1 <- data1 %>% filter(!is.na(Tuberculosis100m) & Tuberculosis100m != "")library(tidyverse)
data1 <- data1 %>% filter(!is.na(MaterMort100m) & MaterMort100m != "")library(tidyverse)
data1 <- data1 %>% filter(!is.na(UndernourishPerPop) & UndernourishPerPop != "")modeloA <- lm(MaterMort100m ~ Tuberculosis100m + EmployPerPop, data = data1)
summary(modeloA)##
## Call:
## lm(formula = MaterMort100m ~ Tuberculosis100m + EmployPerPop,
## data = data1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -500.53 -89.79 -49.00 33.07 1044.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -29.99461 45.62215 -0.657 0.5117
## Tuberculosis100m 0.73456 0.09399 7.815 4.51e-13 ***
## EmployPerPop 2.18668 0.92684 2.359 0.0194 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 185.9 on 179 degrees of freedom
## Multiple R-squared: 0.2935, Adjusted R-squared: 0.2856
## F-statistic: 37.18 on 2 and 179 DF, p-value: 3.141e-14modeloB <- lm(MaterMort100m ~ TasaFertil1mMuje + EmployPerPop, data = data1)
summary(modeloB)##
## Call:
## lm(formula = MaterMort100m ~ TasaFertil1mMuje + EmployPerPop,
## data = data1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -276.87 -71.77 -2.35 37.66 897.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -70.8583 33.3665 -2.124 0.0351 *
## TasaFertil1mMuje 4.3144 0.2606 16.556 <2e-16 ***
## EmployPerPop 0.4119 0.6891 0.598 0.5508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 135.3 on 179 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.6215
## F-statistic: 149.6 on 2 and 179 DF, p-value: < 2.2e-16#PREGUNTA 2 - PRESOS
library(rio)
lkXLSX="https://docs.google.com/spreadsheets/d/1AhloZIapoIhz4-qOgn3u2AXKykKoA9-fDGnhT2CGxt0/edit#gid=1573532387"
data2=import(lkXLSX)data2$casado <- factor(data2$casado, levels = c(0, 1), labels = c("Sí", "No"))library(survival)
data2$survival=with(data2,Surv(time = semanasLibre,event = as.numeric(fueArrestado)))
# que es:
library(magrittr) # needed for pipe %>% ##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extractdata2%>%
rmarkdown::paged_table()library(ggplot2)
library(ggfortify)
#aqui el generico
KM.generico = survfit(survival ~ 1, data = data2)
###graficando
ejeX='SEMANAS\n curva cae cuando alguien es arrestado'
ejeY='Probabilidad \n(PERMANECER LIBRE)'
titulo="Curva de Sobrevivencia: permanecer libre"
autoplot(KM.generico,xlab=ejeX,ylab=ejeY, main = titulo,conf.int = F)
#Investigador A
modelo_A <- coxph(survival ~ edad + casado, data = data2)
summary (modelo_A)## Call:
## coxph(formula = survival ~ edad + casado, data = data2)
##
## n= 432, number of events= 114
##
## coef exp(coef) se(coef) z Pr(>|z|)
## edad -0.06672 0.93545 0.02082 -3.205 0.00135 **
## casadoNo -0.49314 0.61070 0.37269 -1.323 0.18576
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## edad 0.9355 1.069 0.8981 0.9744
## casadoNo 0.6107 1.637 0.2942 1.2678
##
## Concordance= 0.616 (se = 0.027 )
## Likelihood ratio test= 17.27 on 2 df, p=2e-04
## Wald test = 13.99 on 2 df, p=9e-04
## Score (logrank) test = 14.51 on 2 df, p=7e-04aic_modelo <- AIC(modelo_A)
print(aic_modelo)## [1] 1337.494#Investigador B
modelo_B <- coxph(survival ~ edad * casado, data = data2)
summary (modelo_B)## Call:
## coxph(formula = survival ~ edad * casado, data = data2)
##
## n= 432, number of events= 114
##
## coef exp(coef) se(coef) z Pr(>|z|)
## edad -0.05857 0.94311 0.02093 -2.798 0.00514 **
## casadoNo 3.46762 32.06033 2.80648 1.236 0.21662
## edad:casadoNo -0.16260 0.84993 0.11852 -1.372 0.17009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## edad 0.9431 1.06032 0.9052 0.9826
## casadoNo 32.0603 0.03119 0.1309 7849.5624
## edad:casadoNo 0.8499 1.17657 0.6737 1.0722
##
## Concordance= 0.616 (se = 0.026 )
## Likelihood ratio test= 19.76 on 3 df, p=2e-04
## Wald test = 12.61 on 3 df, p=0.006
## Score (logrank) test = 14.58 on 3 df, p=0.002aic_modelo2 <- AIC(modelo_B)
print(aic_modelo2)## [1] 1336.999#PREGUNTA 4 - ADMISIÓN
library(rio)
lkXLSX="https://docs.google.com/spreadsheets/d/1qrRFEAB-jZBUUjn9XwEm8mi-l0l4mJnCWUPX6aXtpz4/edit#gid=0"
data3=import(lkXLSX)data3$admitido=as.factor(data3$admitido)
data3$admitido=factor(data3$admitido,levels=levels(data3$admitido) ,labels=c("No","Si"))
data3$prestigio=as.factor(data3$prestigio)
data3$prestigio=factor(data3$prestigio,levels=levels(data3$prestigio),labels=c("1","2", "3", "4"), ordered = T)
str(data3)## 'data.frame': 400 obs. of 4 variables:
## $ admitido : Factor w/ 2 levels "No","Si": 1 2 2 2 1 2 2 1 2 1 ...
## $ letras : int 380 660 800 640 520 760 560 400 540 700 ...
## $ ciencias : int 361 367 400 319 293 300 298 308 339 392 ...
## $ prestigio: Ord.factor w/ 4 levels "1"<"2"<"3"<"4": 2 2 4 1 1 3 4 3 2 3 ...set.seed(1)
modelo = glm(admitido ~ letras, family = binomial, data = data3)
modelor = glm(admitido ~ letras + ciencias, family = binomial, data = data3)
modelog = glm(admitido ~ letras + ciencias + prestigio, family = binomial, data = data3)
library(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazerstargazer(modelo, modelor,modelog, type = "text")##
## ===============================================
## Dependent variable:
## -----------------------------
## admitido
## (1) (2) (3)
## -----------------------------------------------
## letras 0.004*** 0.003** 0.002**
## (0.001) (0.001) (0.001)
##
## ciencias 0.008** 0.008**
## (0.003) (0.003)
##
## prestigio.L 1.189***
## (0.287)
##
## prestigio.Q 0.232
## (0.252)
##
## prestigio.C -0.099
## (0.212)
##
## Constant -2.901*** -4.949*** -4.882***
## (0.606) (1.075) (1.113)
##
## -----------------------------------------------
## Observations 400 400 400
## Log Likelihood -243.028 -240.172 -229.259
## Akaike Inf. Crit. 490.056 486.344 470.517
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01library(modelsummary)
modelsummary(modelog,
exponentiate = T,
statistic = 'conf.int',
title = "Regresión Logísticas (Coeficientes Exponenciados)",
stars = TRUE,
output = "kableExtra")| (1) | |
|---|---|
| (Intercept) | 0.008*** |
| [0.001, 0.064] | |
| letras | 1.002* |
| [1.000, 1.004] | |
| ciencias | 1.008* |
| [1.002, 1.015] | |
| prestigio.L | 3.285*** |
| [1.897, 5.879] | |
| prestigio.Q | 1.261 |
| [0.764, 2.057] | |
| prestigio.C | 0.906 |
| [0.595, 1.369] | |
| Num.Obs. | 400 |
| AIC | 470.5 |
| BIC | 494.5 |
| Log.Lik. | -229.259 |
| F | 7.228 |
| RMSE | 0.44 |
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 |