diff --git a/README.md b/README.md
index feb633628ea0689a443d2a1695259f62ca2d2111..3985e70fa2bbc2fab0ae575d3d654115a923947d 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,9 @@
 Le dépôt contient différents répertoires :
 
 - `data` pour les données
-- `R`, `Python` et `SAS` pour les sujets et les notebooks à remplir
+- `sujet` pour les scripts ayant engendré les sujets
+
+Il sera mis au jour au cours du semestre.
 
 
 
diff --git a/data/.Rhistory b/data/.Rhistory
deleted file mode 100644
index a7984eee85579db6272e5f762c190a66b37ce7cd..0000000000000000000000000000000000000000
--- a/data/.Rhistory
+++ /dev/null
@@ -1,64 +0,0 @@
-library(survival)
-lung
-library(readr)
-write_csv(lung, "lung.csv")
-knitr::opts_chunk$set(echo = TRUE)
-data(mammals)
-data(mammals, package = "MASS")
-force(mammals)
-libary(tidyverse)
-library(tidyverse)
-ggplot(mammals, aes(x = body, y = brain)) +
-geom_point()
-ggplot(mammals, aes(x = body, y = brain)) +
-geom_point() + theme_classic()
-ggplot(mammals, aes(x = body, y = brain)) +
-geom_point() + theme_bw()
-mammals <- mammals %>%
-mutate(log_body = log(body),
-log_brain = log(brain))
-ggplot(mammals, aes(x = log_body, y = log_brain)) +
-geom_point() + theme_bw()
-library(tidyverse)
-library(rstan)
-options(mc.cores = 4)
-rstan_options(auto_write = TRUE)
-library(saemix)
-theo <- read_csv("theophyllineData.csv",
-col_types = cols(id = col_factor()))
-theo <- theo %>%
-mutate(id_num = as.integer(id))
-bayes_model_ka <- stan_model("bayes_TP2.stan")
-init_func_ka <- function(){
-ka_pop <- 1.86 #psi0_hat[1]
-ke <- 0.086    #psi0_hat[2]
-V <- 33       #psi0_hat[3]
-a <- 1
-ka <- rep(1.8, 12)   #as.matrix(psi(sfit))[,1]
-tau <- 1             #sqrt(diag(sfit@results@omega))[1]
-eta <- (ka - ka_pop)/tau
-list(ka_pop = ka_pop, ke = ke, V = V, a = a, eta = eta, tau = tau)}
-posterior_ka <- sampling(bayes_model_ka, data = stan_data,
-init = init_func_ka,
-chains = 4, warmup = 5000,
-iter = 10000,
-refresh = 1000)
-stan_data <- list(ntot = nrow(theo), N = 12, y = theo$concentration,
-id = as.integer(theo$id), tps = theo$time, D = 320)
-posterior_ka <- sampling(bayes_model_ka, data = stan_data,
-init = init_func_ka,
-chains = 4, warmup = 5000,
-iter = 10000,
-refresh = 1000)
-bayes_model_ka <- stan_model("bayes_mixed_ka.stan")
-library(nycflights13)
-?flights
-head(flights)
-write.csv2(flights, row.names = FALSE)
-path <- "/Users/ppudlo/git/m1-donnees-tp/data"
-setwd(path)
-write.csv2(flights, file="fligths.csv", row.names = FALSE)
-write.csv2(airlines, file="airlines.csv", row.names = FALSE)
-write.csv2(airports, file="airports.csv", row.names = FALSE)
-write.csv2(planes, file="planes.csv", row.names = FALSE)
-write.csv2(weather, file="weather.csv", row.names = FALSE)