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Arthur DANJOU
03bf0a4db2
- Updated comments and code formatting in `3-td_ggplot2 - enonce.Rmd` for clarity. - Enhanced code structure in `4-td_graphiques - enonce.Rmd` by organizing options and library calls. - Replaced pipe operator `%>%` with `|>` in `Code_Lec3.Rmd` for consistency with modern R syntax. - Cleaned up commented-out code and ensured consistent spacing in ggplot calls.
95 lines
2.4 KiB
Plaintext
95 lines
2.4 KiB
Plaintext
```{r}
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setwd("/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP4")
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set.seed(0911)
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library(ggplot2)
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library(gridExtra)
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library(cowplot)
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library(plotly) # interactif plot
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library(ggfortify) # diagnostic plot
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library(forestmodel) # plot odd ratio
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library(arm) # binnedplot diagnostic plot in GLM
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library(knitr)
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library(dplyr)
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library(tidyverse)
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library(tidymodels)
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library(broom) # funtion augment to add columns to the original data that was modeled
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library(effects) # plot effect of covariate/factor
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library(questionr) # odd ratio
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library(lmtest) # LRtest
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library(survey) # Wald test
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library(vcdExtra) # deviance test
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library(rsample) # for data splitting
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library(glmnet)
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library(nnet) # multinom, glm
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library(caret)
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library(ROCR)
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# library(PRROC) autre package pour courbe roc et courbe pr
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library(ISLR) # dataset for statistical learning
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ggplot2::theme_set(ggplot2::theme_light()) # Set the graphical theme
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```
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```{r}
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car <- read.table("car_income.txt", header = TRUE, sep = ";")
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car |> rmarkdown::paged_table()
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summary(car)
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```
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```{r}
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model_purchase <- glm(purchase ~ ., data = car, family = "binomial")
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summary(model_purchase)
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```
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```{r}
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p1 <- car |>
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ggplot(aes(y = purchase, x = income + age)) +
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geom_point(alpha = .15) +
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geom_smooth(method = "lm") +
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ggtitle("Linear regression model fit") +
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xlab("Income") +
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ylab("Probability of Purchase")
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p2 <- car |>
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ggplot(aes(y = purchase, x = income + age)) +
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geom_point(alpha = .15) +
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geom_smooth(method = "glm", method.args = list(family = "binomial")) +
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ggtitle("Logistic regression model fit") +
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xlab("Income") +
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ylab("Probability of Purchase")
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ggplotly(p1)
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ggplotly(p2)
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```
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```{r}
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car <- car |>
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mutate(old = ifelse(car$age > 3, 1, 0))
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car <- car |>
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mutate(rich = ifelse(car$income > 40, 1, 0))
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model_old <- glm(purchase ~ age + income + rich + old, data = car, family = "binomial")
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summary(model_old)
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```
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# Diabetes in Pima Indians
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```{r}
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library(MASS)
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pima.tr <- Pima.tr
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pima.te <- Pima.te
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model_train_pima <- glm(type ~ npreg + glu + bp + skin + bmi + ped + age, data = pima.tr, family = "binomial")
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summary(model_train_pima)
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```
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```{r}
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pima.te$pred <- predict(model_train_pima, newdata = pima.te, type = "response")
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pima.te$pred <- ifelse(pima.te$pred > 0.55, "Yes", "No")
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pima.te$pred <- as.factor(pima.te$pred)
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pima.te$type <- as.factor(pima.te$type)
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# Confusion matrix
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confusionMatrix(data = pima.te$type, reference = pima.te$pred, positive = "Yes")
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```
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