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87 lines
2.2 KiB
Plaintext
87 lines
2.2 KiB
Plaintext
```{r}
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setwd('/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP2')
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```
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# Question 1 : Import dataset and check variables
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```{r}
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library(dplyr)
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cepages <- read.csv("Cepages B TP2.csv", header = TRUE, sep = ";", dec = ",")
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cepages$Couleur <- as.factor(cepages$Couleur)
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cepages$Origine <- as.factor(cepages$Origine)
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cepages <- cepages %>% mutate(across(where(is.character), as.numeric))
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cepages <- cepages %>% mutate(across(where(is.integer), as.numeric))
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paged_table(cepages)
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```
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# Question 2 : Table of counts
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```{r}
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table(cepages$Origine, cepages$Couleur)
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```
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# Question 3
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## Display the table of average Ph according to couleur and average ph
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```{r}
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tapply(cepages$pH, list(cepages$Couleur), mean)
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```
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## Display the table of average pH according to couleur and origine
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```{r}
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tapply(cepages$pH, list(cepages$Couleur, cepages$Origine), mean)
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```
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# Question 4 : Regression lines of ph over AcTol for different Color
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```{r}
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library(ggplot2)
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ggplot(cepages, aes(x = AcTot, y = pH, color = Couleur)) +
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geom_point(col = 'red', size = 0.5) +
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geom_smooth(method = "lm", se = F)
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ggplot(cepages, aes(y = pH, x = AcTot, colour = Couleur, fill = Couleur)) +
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geom_boxplot(alpha = 0.5, outlier.alpha = 0)
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```
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# Question 5 : Regression Ligne of pH over AcTot for different Origine
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```{r}
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ggplot(cepages, aes(x = AcTot, y = pH, color = Origine)) +
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geom_smooth(method = 'lm', se = F) +
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geom_point(col = 'red', size = 0.5)
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ggplot(cepages, aes(y = pH, x = AcTot, colour = Origine, fill = Origine)) +
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geom_boxplot(alpha = 0.5, outlier.alpha = 0)
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```
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# Question 6 : ANOVA
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```{r}
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model_full <- lm(pH ~ Couleur, data = cepages)
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summary(model_full)
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```
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```{r}
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autoplot(model_full, 1:4)
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```
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[P1] is verified as the 'Residuals vs Fitted' plot shows that the points are well distributed around 0
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[P2] is verified as the 'Scale-Location' plot shows that the points are well distributed around 1
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[P4] is verified as the 'QQPlot' is aligned with the 'y=x' line
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```{r}
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set.seed(12)
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durbinWatsonTest(model_full)
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```
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[P3] is verified as the p-value is 0.7 > 0.05, so we do not reject H0 so the residuals are not auto-correlated
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# Bonus : Type II Test
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```{r}
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library(car)
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Anova(model_full)
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```
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