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regressao_logistica_ML.r
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regressao_logistica_ML.r
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#Clear existing data and graphics
rm(list=ls())
graphics.off()
urlfile <- "https://raw.githubusercontent.com/edsonzmartinez/cursoR/main/lowbwt.csv"
dados <- read.csv2(urlfile)
head(dados)
library(carot)
library(pROC)
# Visitas ao médico
table(dados$FTV)
dados$FTV2 <- ifelse(dados$FTV<3,dados$FTV,2)
table(dados$FTV2)
# Partos prematuros
table(dados$PTL)
dados$PTL2 <- ifelse(dados$PTL==0,0,1)
table(dados$PTL2)
# Logistic regression
modelo <- factor(LOW) ~ AGE + LWT + factor(RACE) + factor(SMOKE) + factor(PTL2) + factor(HT) + factor(UI) + factor(FTV2)
lmodel <- glm(modelo,data=dados,family="binomial")
summary(lmodel)
# Predicted
pred <- predict(lmodel)
summary(pred)
# Probs
prob <- 1/(1+exp(-pred))
summary(prob)
# Validation
spec <- c(train = .75, test = .25)
g <- sample(cut(seq(nrow(dados)),nrow(dados)*cumsum(c(0,spec)),labels = names(spec)))
res <- split(dados, g)
lmodelt <- glm(modelo,data=res$train,family="binomial")
summary(lmodelt)
pred <- predict(lmodelt,newdata=res$test)
pred_y <- as.numeric(pred>0)
true_y <- as.numeric(res$test$LOW==1)
# Confusion matrix
true_pos <- (true_y==1) & (pred_y==1)
true_neg <- (true_y==0) & (pred_y==0)
false_pos <- (true_y==0) & (pred_y==1)
false_neg <- (true_y==1) & (pred_y==0)
conf_mat <- matrix(c(sum(true_pos),sum(false_pos),sum(false_neg),sum(true_neg)),2,2)
colnames(conf_mat) <- c("Yhat = 1","Yhat = 0")
rownames(conf_mat) <- c("Y = 1","Y = 0")
conf_mat
# ROC curve
roccurve <- roc(true_y,pred)
plot(roccurve)
cbind(roccurve$thresholds,roccurve$sensitivities,roccurve$specificities
###
### Cross-validation K-fold
###
# Specify the cross-validation method
ctrl <- trainControl(method = "cv", number = 5)
# Fit a regression model and use k-fold CV to evaluate performance
modelK <- train(modelo,data=dados,family="binomial", method = "glm", trControl = ctrl)
# view summary of k-fold CV
print(modelK)
modelK$resample
###
# Leave one out cross validation (LOOCV)
###
# defining training control
# as Leave One Out Cross Validation
ctrl <- trainControl(method = "LOOCV")
# training the model by assigning sales column
# as target variable and rest other column
# as independent variable
modelLOOCV <- train(modelo,data=dados,family="binomial", method = "glm", trControl = ctrl)
# printing model performance metrics
# along with other details
print(modelLOOCV)
modelLOOCV$resample