C2

Logística y Cox

library(MASS)
library(magrittr)

library(rio)
data="https://github.com/CarlosChavarri23/CONTROL-/blob/main/dataAdmit%20(1).xlsx?raw=true"
admi =import(data)
admi=na.omit(admi)

str(admi)

## 'data.frame':    400 obs. of  4 variables:
##  $ admitido : chr  "no" "si" "si" "si" ...
##  $ gre      : num  380 660 800 640 520 760 560 400 540 700 ...
##  $ gpa      : num  3.61 3.67 4 3.19 2.93 3 2.98 3.08 3.39 3.92 ...
##  $ prestigio: chr  "Bajo" "Bajo" "MuyAlto" "MuyBajo" ...

admi$admitido=as.factor(admi$admitido)
admi$prestigio=as.ordered(admi$prestigio)
str(admi)

## 'data.frame':    400 obs. of  4 variables:
##  $ admitido : Factor w/ 2 levels "no","si": 1 2 2 2 1 2 2 1 2 1 ...
##  $ gre      : num  380 660 800 640 520 760 560 400 540 700 ...
##  $ gpa      : num  3.61 3.67 4 3.19 2.93 3 2.98 3.08 3.39 3.92 ...
##  $ prestigio: Ord.factor w/ 4 levels "Alto"<"Bajo"<..: 2 2 3 4 4 1 3 1 2 1 ...

mylogit <- glm(admitido ~ gre + gpa + prestigio, 
               data = admi, family = "binomial")
#
summary(mylogit)

## 
## Call:
## glm(formula = admitido ~ gre + gpa + prestigio, family = "binomial", 
##     data = admi)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.6268  -0.8662  -0.6388   1.1490   2.0790  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -4.881757   1.113111  -4.386 1.16e-05 ***
## gre          0.002264   0.001094   2.070   0.0385 *  
## gpa          0.804038   0.331819   2.423   0.0154 *  
## prestigio.L -0.287974   0.257744  -1.117   0.2639    
## prestigio.Q -0.443351   0.252796  -1.754   0.0795 .  
## prestigio.C -1.094920   0.245833  -4.454 8.43e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 499.98  on 399  degrees of freedom
## Residual deviance: 458.52  on 394  degrees of freedom
## AIC: 470.52
## 
## Number of Fisher Scoring iterations: 4

sdVIs=apply(admi[,c("gre","gpa", "prestigio")],2,sd)

## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introducidos por coerción

list(LogitSt=sdVIs*coef(mylogit)[c(2,3,4)])%>%
    data.frame()

##             LogitSt
## gre       0.2615786
## gpa       0.3059900
## prestigio        NA