LIBRERÍAS

LIBRERIAS

library(rio)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(car)

## Loading required package: carData

## 
## Attaching package: 'car'

## The following object is masked from 'package:dplyr':
## 
##     recode

library(dplyr)
library(ggplot2)
library(data.table)

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:dplyr':
## 
##     between, first, last

library(devtools)

## Loading required package: usethis

library(foreign)
library(corrplot)

## corrplot 0.92 loaded

library(DescTools)

## 
## Attaching package: 'DescTools'

## The following object is masked from 'package:data.table':
## 
##     %like%

## The following object is masked from 'package:car':
## 
##     Recode

library(lm.beta)
library(lmtest)

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

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=stargazer

library(ggfortify)
library(see)
library(patchwork)
library(performance)
library(tidyverse)

## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──

## ✔ tibble  3.1.8     ✔ purrr   0.3.4
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ data.table::between() masks dplyr::between()
## ✖ dplyr::filter()       masks stats::filter()
## ✖ data.table::first()   masks dplyr::first()
## ✖ dplyr::lag()          masks stats::lag()
## ✖ data.table::last()    masks dplyr::last()
## ✖ car::recode()         masks dplyr::recode()
## ✖ purrr::some()         masks car::some()
## ✖ purrr::transpose()    masks data.table::transpose()

library(see)
library(patchwork)
library(performance)

LOGÍSTICO LINEAL BINARIO

PARA CONVERTIR UNA VARIABLE A DUMMY VD: RETORNO A CLASES data$retorno=as.factor(data$P2_2) levels(data$retorno) = c("no", "si") table(data$retorno) VD: Retorno VI: El docente vive con personas de la tercera edad Se convierte dummy: data$P1_4=recode(data$P1_4,“1=1;2=0”) data$P1_4=as.numeric(data$P1_4) table(data$P1_4) AHORA SE GENERA EL MODELO: modelo1 = glm(retorno ~ P1_4, family = binomial(link=“logit”) ,data = data) summary(modelo1)

LOGISTICO ORDINAL NIVEL DE SALARIO: PRETENDE UN ORDEN RECODIFICACIÓN DE LA DEPENDIENTE: NIVEL DE SALARIO

data$salario_actual_ordinal = cut(data$salario_actual, breaks = c(0, 24000,28875, 36938,135000), include.lowest = T, ordered_result = T, labels = c(“Muy Bajo”, “Bajo”, “Alto”, “Muy Alto”))

table(data$salario_actual_ordinal) Realizamos el modelo library(MASS) modelo <- polr(salario_actual_ordinal ~ sexo_Mujer + educ, data = data, Hess=T) summary(modelo) MODELO LOGÍSTICO MULTINOMIAL library(rattle.data) data(wine) require(nnet) MODELO: multinom.fit <- multinom(Type ~ Alcohol + Color -1, data = train)

LIBRERÍAS

2022-10-12