Bùi Minh Ảnh ktra 3

data(iris)

model <- lm(Petal.Length ~ Sepal.Length + Sepal.Width, data = iris)

summary(model)

## 
## Call:
## lm(formula = Petal.Length ~ Sepal.Length + Sepal.Width, data = iris)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.25582 -0.46922 -0.05741  0.45530  1.75599 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -2.52476    0.56344  -4.481 1.48e-05 ***
## Sepal.Length  1.77559    0.06441  27.569  < 2e-16 ***
## Sepal.Width  -1.33862    0.12236 -10.940  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6465 on 147 degrees of freedom
## Multiple R-squared:  0.8677, Adjusted R-squared:  0.8659 
## F-statistic:   482 on 2 and 147 DF,  p-value: < 2.2e-16

library(mlbench)
library(pROC)

## Type 'citation("pROC")' for a citation.

## 
## Attaching package: 'pROC'

## The following objects are masked from 'package:stats':
## 
##     cov, smooth, var

# Load dữ liệu bệnh tim
data(PimaIndiansDiabetes)

Heart <- PimaIndiansDiabetes

# Xây dựng mô hình logistic
model_logit <- glm(diabetes ~ ., data = Heart, family = binomial)

# Dự đoán xác suất
prob <- predict(model_logit, type = "response")

# Vẽ ROC
roc_obj <- roc(Heart$diabetes, prob)

## Setting levels: control = neg, case = pos

## Setting direction: controls < cases

plot(roc_obj, main="ROC Curve")

# Tính AUC
auc(roc_obj)

## Area under the curve: 0.8394