Regresión logística

library(pacman)
p_load("DT","xfun","ggplot2", "readr")

camarones <- read_csv("camarones.csv")

## Rows: 12 Columns: 2

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## dbl (2): AlimentoDiario, Exito

## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

datatable(camarones)

tabla <- table(camarones$Exito)
tabla

## 
## 0 1 
## 9 3

colores <- NULL
colores[camarones$Exito == 0] <- "green"
colores[camarones$AlimentoDiario == 1] <- "red"
plot(camarones$Exito, camarones$AlimentoDiario, pch=21, bg= colores,
     xlab = "Llego a los 12gr", ylab = "Alimento Diario dado")
legend("bottomleft", c("No exitos", "Si exitos"), pch=21, col = c("green", "red")      )

regresion <- glm(Exito ~ AlimentoDiario, data=camarones, family= binomial)
summary(regresion)

## 
## Call:
## glm(formula = Exito ~ AlimentoDiario, family = binomial, data = camarones)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.28965  -0.68424  -0.39705  -0.00008   2.00729  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)
## (Intercept)    -35.1229    25.8776  -1.357    0.175
## AlimentoDiario   0.1194     0.0901   1.325    0.185
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 13.496  on 11  degrees of freedom
## Residual deviance: 11.311  on 10  degrees of freedom
## AIC: 15.311
## 
## Number of Fisher Scoring iterations: 5

camarones_new <- data.frame(Exito= seq(0,1,0.2) )
camarones_new

##   Exito
## 1   0.0
## 2   0.2
## 3   0.4
## 4   0.6
## 5   0.8
## 6   1.0