Skip to content
Snippets Groups Projects
Commit bce11713 authored by Pierre Pudlo's avatar Pierre Pudlo
Browse files

readme

parent c1d83f8b
No related branches found
No related tags found
No related merge requests found
...@@ -3,7 +3,9 @@ ...@@ -3,7 +3,9 @@
Le dépôt contient différents répertoires : Le dépôt contient différents répertoires :
- `data` pour les données - `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.
......
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)
0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment