mirror of
https://github.com/ArthurDanjou/ArtStudies.git
synced 2026-01-14 15:54:13 +01:00
96 lines
2.4 KiB
Plaintext
96 lines
2.4 KiB
Plaintext
```{r}
|
|
setwd('/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP4')
|
|
|
|
set.seed(0911)
|
|
library(ggplot2)
|
|
library(gridExtra)
|
|
library(cowplot)
|
|
library(plotly) # interactif plot
|
|
library(ggfortify) # diagnostic plot
|
|
library(forestmodel) # plot odd ratio
|
|
library(arm) # binnedplot diagnostic plot in GLM
|
|
|
|
library(knitr)
|
|
library(dplyr)
|
|
library(tidyverse)
|
|
library(tidymodels)
|
|
library(broom) # funtion augment to add columns to the original data that was modeled
|
|
library(effects) # plot effect of covariate/factor
|
|
library(questionr) # odd ratio
|
|
|
|
library(lmtest) # LRtest
|
|
library(survey) # Wald test
|
|
library(vcdExtra) # deviance test
|
|
|
|
library(rsample) # for data splitting
|
|
library(glmnet)
|
|
library(nnet) # multinom, glm
|
|
library(caret)
|
|
library(ROCR)
|
|
#library(PRROC) autre package pour courbe roc et courbe pr
|
|
library(ISLR) # dataset for statistical learning
|
|
|
|
ggplot2::theme_set(ggplot2::theme_light())# Set the graphical theme
|
|
```
|
|
```{r}
|
|
car <- read.table('car_income.txt', header = TRUE, sep = ';')
|
|
car %>% rmarkdown::paged_table()
|
|
summary(car)
|
|
```
|
|
|
|
```{r}
|
|
model_purchase <- glm(purchase ~ ., data = car, family = "binomial")
|
|
summary(model_purchase)
|
|
```
|
|
|
|
```{r}
|
|
p1 <- car %>%
|
|
ggplot(aes(y = purchase, x = income + age)) +
|
|
geom_point(alpha = .15) +
|
|
geom_smooth(method = "lm") +
|
|
ggtitle("Linear regression model fit") +
|
|
xlab("Income") +
|
|
ylab("Probability of Purchase")
|
|
|
|
|
|
p2 <- car %>%
|
|
ggplot(aes(y = purchase, x = income + age)) +
|
|
geom_point(alpha = .15) +
|
|
geom_smooth(method = "glm", method.args = list(family = "binomial")) +
|
|
ggtitle("Logistic regression model fit") +
|
|
xlab("Income") +
|
|
ylab("Probability of Purchase")
|
|
|
|
ggplotly(p1)
|
|
ggplotly(p2)
|
|
```
|
|
|
|
```{r}
|
|
car <- car %>%
|
|
mutate(old = ifelse(car$age > 3, 1, 0))
|
|
car <- car %>%
|
|
mutate(rich = ifelse(car$income > 40, 1, 0))
|
|
model_old <- glm(purchase ~ age + income + rich + old, data = car, family = "binomial")
|
|
summary(model_old)
|
|
```
|
|
|
|
# Diabetes in Pima Indians
|
|
```{r}
|
|
library(MASS)
|
|
pima.tr <- Pima.tr
|
|
pima.te <- Pima.te
|
|
|
|
model_train_pima <- glm(type ~ npreg + glu + bp + skin + bmi + ped + age, data = pima.tr, family = "binomial")
|
|
summary(model_train_pima)
|
|
```
|
|
```{r}
|
|
|
|
pima.te$pred <- predict(model_train_pima, newdata = pima.te, type = "response")
|
|
pima.te$pred <- ifelse(pima.te$pred > 0.5, "Yes", "No")
|
|
pima.te$pred <- as.factor(pima.te$pred)
|
|
pima.te$type <- as.factor(pima.te$type)
|
|
|
|
# Confusion matrix
|
|
confusionMatrix(data = pima.te$type, reference = pima.te$pred)
|
|
```
|