Laboratorio Semana 2: Ernesto Cancho-Rodríguez

Se trabaja un prueba de modelos avanzados…

require(ggplot2)
require(sandwich)
require(msm)
library("ggplot2")
library("sandwich")
library("msm")

Cargo de datos…

p <- read.csv("https://stats.idre.ucla.edu/stat/data/poisson_sim.csv")
#p
summary(p)

       id           num_awards        prog      
 Min.   :  1.00   Min.   :0.00   Min.   :1.000  
 1st Qu.: 50.75   1st Qu.:0.00   1st Qu.:2.000  
 Median :100.50   Median :0.00   Median :2.000  
 Mean   :100.50   Mean   :0.63   Mean   :2.025  
 3rd Qu.:150.25   3rd Qu.:1.00   3rd Qu.:2.250  
 Max.   :200.00   Max.   :6.00   Max.   :3.000  
      math      
 Min.   :33.00  
 1st Qu.:45.00  
 Median :52.00  
 Mean   :52.65  
 3rd Qu.:59.00  
 Max.   :75.00  

Cambio de tipo de dato para las columnas

p <- within(p, {
  prog <- factor(prog, levels=1:3, labels=c("General", "Academic", 
                                                     "Vocational"))
  id <- factor(id)
})
summary(p)

       id        num_awards           prog          math      
 1      :  1   Min.   :0.00   General   : 45   Min.   :33.00  
 2      :  1   1st Qu.:0.00   Academic  :105   1st Qu.:45.00  
 3      :  1   Median :0.00   Vocational: 50   Median :52.00  
 4      :  1   Mean   :0.63                    Mean   :52.65  
 5      :  1   3rd Qu.:1.00                    3rd Qu.:59.00  
 6      :  1   Max.   :6.00                    Max.   :75.00  
 (Other):194                                                  

EDA: La siguiente tabla muestra el número promedio de premios por tipo de programa y otros EDA…

with(p, tapply(num_awards, prog, function(x) {
  sprintf("M (SD) = %1.2f (%1.2f)", mean(x), sd(x))
}))

               General               Academic 
"M (SD) = 0.20 (0.40)" "M (SD) = 1.00 (1.28)" 
            Vocational 
"M (SD) = 0.24 (0.52)" 

EDA: Histograma: Eje X: Nro de premios, Eje Y: Cantidad de Personas, Color: Programa (prog)

ggplot(p, aes(num_awards, fill = prog)) +
  geom_histogram(binwidth=.5, position="dodge")

MODELAR A CONTINUACION.