Onnowpurbo at 09:07, 29 November 2019

2019-11-29T09:07:05Z

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

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2019-11-29T08:42:23Z

Created page with " # Ref: http://www.sthda.com/english/articles/36-classification-methods-essentials/151-logistic-regression-essentials-in-r/ ==Referensi== * http://www.sthda.com/englis..."

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

==Referensi==

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

==Pranala Menarik==

* [[R]]

R Regression: Logistic Regression - Revision history

Onnowpurbo at 09:07, 29 November 2019

Onnowpurbo: Created page with " # Ref: http://www.sthda.com/english/articles/36-classification-methods-essentials/151-logistic-regression-essentials-in-r/ ==Referensi== * http://www.sthda.com/englis..."