oliviergimenez

##################################################################################
# How to calculate wAIC with JAGS: Linear regression example                     #
# https://sourceforge.net/p/mcmc-jags/discussion/610036/thread/8211df61/#ea5c    #
##################################################################################

#--- Simulate data
set.seed(2020) # set seed
n <- 100 # sample size
x <- sort(rnorm(n)) # covariate values
int <- 30 # true intercept

oliviergimenez

# https://twitter.com/GeoffZahn/status/1318653420027273216?s=20
library(tidyverse)
data_frame(t=seq(-pi, 0, .001),
           x1=16*(sin(t))^2,
           x2=-x1,
           y=13*cos(t) -5 * cos(2*t) -2*cos(3*t) - cos(4*t)) %>%
  gather(side, x, x1, x2) %>%
  ggplot(aes(x,y)) +
  geom_polygon(fill="red") +
  coord_fixed() +

oliviergimenez

# map highways in Switzerland - France 

# load packages
library(tidyverse)
library(sf)
library(osmdata)
library(mapview)

# define zone of interest
lonmin <- 6

oliviergimenez

# simple example to mimic what predict.gam does
library(mgcv) # GAM package, see Wood's book https://www.taylorfrancis.com/books/9781315370279
dat <- MASS::mcycle # get mcycle data
head(dat)
# set up a smoother
sm <- smoothCon(s(times, k = 10), data = dat, knots = NULL)[[1]]
# use it to fit a regression spline model
beta <- coef(lm(dat$accel ~ sm$X - 1))
# plot  data
plot(dat$times, dat$accel)

oliviergimenez

# code from https://gist.github.com/d-qn/4f1c4ed80a4fd6a76cd1153c89f56134

library(tidyverse)
library(sf)
library(osmdata) # to get openstreetmap geo data

# settings
# olivier: get data on Montpellier
bb <- getbb('Montpellier, France') # define the bbox, will be used to fetch OSM data within that box
dest_proj <- 2056