---

---

library(foreign)
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.3.2

library(MASS)
library(openxlsx)

## Warning: package 'openxlsx' was built under R version 4.3.1

info <- read.csv("C:/Users/Mauri/Downloads/Alumnos1.csv")

LDA

info$pasa<-factor(info$pasa, levels = c(0,1), labels = c("No pasa","Pasa"))

dis=lda(pasa~futbol+tenis+escuela+ingles, data=info,prior=c(0.5,0.5))
dis

## Call:
## lda(pasa ~ futbol + tenis + escuela + ingles, data = info, prior = c(0.5, 
##     0.5))
## 
## Prior probabilities of groups:
## No pasa    Pasa 
##     0.5     0.5 
## 
## Group means:
##           futbol    tenis  escuela   ingles
## No pasa 4.520000 2.560000 2.080000 6.600000
## Pasa    5.448276 2.724138 2.068966 5.310345
## 
## Coefficients of linear discriminants:
##                  LD1
## futbol   0.243469380
## tenis   -0.092641981
## escuela -0.006693476
## ingles  -0.289571372

#Nueva observación Supongamos que entra un alumno nuevo. Y que: hrs_estudio =4.3 hrs_fiesta =1.5 hrs_camino =1.3 calif_mate =8.0

Creamos el perfil del estudiante:

nuevo.alumno=rbind(c(4.3,1.5,1.3,8.0))
colnames(nuevo.alumno)=colnames(info[,2:5])
nuevo.alumno=data.frame(nuevo.alumno)

predict(dis,newdata =nuevo.alumno)

## $class
## [1] No pasa
## Levels: No pasa Pasa
## 
## $posterior
##     No pasa      Pasa
## 1 0.5935033 0.4064967
## 
## $x
##          LD1
## 1 -0.6477027