View source for R Regression: Logistic Regression

 # Ref: http://www.sthda.com/english/articles/36-classification-methods-essentials/151-logistic-regression-essentials-in-r/


 install.packages("mlbench")
 # Load the data set PIMA INDIAN
 data("PimaIndiansDiabetes2", package = "mlbench")
 # Inspect the data
 head(PimaIndiansDiabetes2, 4)


 library(tidyverse)
 library(caret)
 theme_set(theme_bw())


 # PREPARE DATA
 # Load the data and remove NAs
 data("PimaIndiansDiabetes2", package = "mlbench")
 PimaIndiansDiabetes2 <- na.omit(PimaIndiansDiabetes2)
 # Inspect the data
 sample_n(PimaIndiansDiabetes2, 3)
 # Split the data into training and test set
 set.seed(123)
 training.samples <- PimaIndiansDiabetes2$diabetes %>% 
   createDataPartition(p = 0.8, list = FALSE)
 train.data  <- PimaIndiansDiabetes2[training.samples, ]
 test.data <- PimaIndiansDiabetes2[-training.samples, ]



 # Fit the model
 # The R function glm(), for generalized linear model, can be used to compute logistic regression.
 # You need to specify the option family = binomial, which tells to R that we want to fit logistic regression.
 model <- glm( diabetes ~., data = train.data, family = binomial)
 # Summarize the model
 summary(model)
 # Make predictions
 probabilities <- model %>% predict(test.data, type = "response")
 predicted.classes <- ifelse(probabilities > 0.5, "pos", "neg")
 # Model accuracy
 mean(predicted.classes == test.data$diabetes)




 # Simole Logistic Regression
 model <- glm( diabetes ~ glucose, data = train.data, family = binomial)
 summary(model)$coef



 # Prediction
 newdata <- data.frame(glucose = c(20,  180))
 probabilities <- model %>% predict(newdata, type = "response")
 predicted.classes <- ifelse(probabilities > 0.5, "pos", "neg")
 predicted.classes


 # PLOT Prediction Function
 train.data %>%
   mutate(prob = ifelse(diabetes == "pos", 1, 0)) %>%
   ggplot(aes(glucose, prob)) +
   geom_point(alpha = 0.2) +
   geom_smooth(method = "glm", method.args = list(family = "binomial")) +
   labs(
     title = "Logistic Regression Model", 
     x = "Plasma Glucose Concentration",
     y = "Probability of being diabete-pos"
   )




 #
 # Multiple Logistic Regression
 #
 model <- glm( diabetes ~ glucose + mass + pregnant, 
               data = train.data, family = binomial)
 summary(model)$coef 

 # Model Summary
 model <- glm( diabetes ~., data = train.data, family = binomial)
 summary(model)$coef
 # alternate show
 coef(model)
 summary(model )$coef





 # UBAH MODEL
 model <- glm( diabetes ~ pregnant + glucose + pressure + mass + pedigree, 
               data = train.data, family = binomial)



 # Predict
 probabilities <- model %>% predict(test.data, type = "response")
 head(probabilities)
 # check class
 contrasts(test.data$diabetes)

 # Predict the class of individuals:
 predicted.classes <- ifelse(probabilities > 0.5, "pos", "neg")
 head(predicted.classes)


 # Assessing model accuracy
 mean(predicted.classes == test.data$diabetes)





 # You can also fit generalized additive models (Chapter @ref(polynomial-and-spline-regression)),
 # when linearity of the predictor cannot be assumed. This can be done using the mgcv package:
 library("mgcv")
 # Fit the model
 gam.model <- gam(diabetes ~ s(glucose) + mass + pregnant,
                  data = train.data, family = "binomial")
 # Summarize model
 summary(gam.model )
 # Make predictions
 probabilities <- gam.model %>% predict(test.data, type = "response")
 predicted.classes <- ifelse(probabilities> 0.5, "pos", "neg")
 # Model Accuracy
 mean(predicted.classes == test.data$diabetes)








==Referensi==

* http://www.sthda.com/english/articles/36-classification-methods-essentials/151-logistic-regression-essentials-in-r/

==Pranala Menarik==

* [[R]]