La regresión logística es un modelo estadistico de clasificacion binaria, que estima la probabilidad de que ocurra un evento (Valor 1) frente a que no ocurra (Valor 0), en función de variables independientes.
#install.packages("titanic")
library("titanic")
#install.packages("caret")
library("caret")## Loading required package: ggplot2## Loading required package: lattice#install.packages("tidyverse")
library("tidyverse")## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.4 ✔ tibble 3.3.0
## ✔ purrr 1.1.0 ✔ tidyr 1.3.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::lift() masks caret::lift()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdf<-titanic_trainsummary(df)## PassengerId Survived Pclass Name
## Min. : 1.0 Min. :0.0000 Min. :1.000 Length:891
## 1st Qu.:223.5 1st Qu.:0.0000 1st Qu.:2.000 Class :character
## Median :446.0 Median :0.0000 Median :3.000 Mode :character
## Mean :446.0 Mean :0.3838 Mean :2.309
## 3rd Qu.:668.5 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :891.0 Max. :1.0000 Max. :3.000
##
## Sex Age SibSp Parch
## Length:891 Min. : 0.42 Min. :0.000 Min. :0.0000
## Class :character 1st Qu.:20.12 1st Qu.:0.000 1st Qu.:0.0000
## Mode :character Median :28.00 Median :0.000 Median :0.0000
## Mean :29.70 Mean :0.523 Mean :0.3816
## 3rd Qu.:38.00 3rd Qu.:1.000 3rd Qu.:0.0000
## Max. :80.00 Max. :8.000 Max. :6.0000
## NA's :177
## Ticket Fare Cabin Embarked
## Length:891 Min. : 0.00 Length:891 Length:891
## Class :character 1st Qu.: 7.91 Class :character Class :character
## Mode :character Median : 14.45 Mode :character Mode :character
## Mean : 32.20
## 3rd Qu.: 31.00
## Max. :512.33
## str(df)## 'data.frame': 891 obs. of 12 variables:
## $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : int 0 1 1 1 0 0 0 0 1 1 ...
## $ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Ticket : chr "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : chr "" "C85" "" "C123" ...
## $ Embarked : chr "S" "C" "S" "S" ...df<- df[,c("Survived", "Pclass","Sex","Age")]
df<- na.omit(df)
df$Survived<-as.factor(df$Survived)
df$Sex<-as.factor(df$Sex)
df$Pclass<-as.factor(df$Pclass)modelo<- glm(Survived~ ., data=df, family=binomial)
summary(modelo) ##
## Call:
## glm(formula = Survived ~ ., family = binomial, data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.777013 0.401123 9.416 < 2e-16 ***
## Pclass2 -1.309799 0.278066 -4.710 2.47e-06 ***
## Pclass3 -2.580625 0.281442 -9.169 < 2e-16 ***
## Sexmale -2.522781 0.207391 -12.164 < 2e-16 ***
## Age -0.036985 0.007656 -4.831 1.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 964.52 on 713 degrees of freedom
## Residual deviance: 647.28 on 709 degrees of freedom
## AIC: 657.28
##
## Number of Fisher Scoring iterations: 5prueba<- data.frame(Pclass= as.factor(c(1,3)),
Sex= as.factor(c("female","male")), Age=c(25,40))
probabilidad<- predict(modelo,newdata=prueba, type="response")
cbind(prueba,Probabilidad_Sobrevive=probabilidad)## Pclass Sex Age Probabilidad_Sobrevive
## 1 1 female 25 0.94544163
## 2 3 male 40 0.05701133