data-raw/bikesharing-scripts/03_bikes_atom.R

In ooelrich/oscbvar: Stuff for Bayesian vector autoregressions

############################################################
### GENERATE AGENT PREDICTIONS                           ###
############################################################

# Settings for generating data (starting time, window_lengt, rolling window)
agent_controller <- list(1, 200, FALSE)

# starting at 200 this takes 58 minutes for BART (no, more like SEVERAL HOURS)
aa <- Sys.time()

bikes_bart <- bikes_bart(
    agc = agent_controller,
    include_intercept = FALSE,
    nrep = 10000,
    nburn = 5000
)

bikes_sv <- bikes_sv(
    log_scale = TRUE,
    agc = agent_controller,
    include_intercept = TRUE
)

bikes_reg <- bikes_regression(
    log_scale = TRUE,
    agc = agent_controller
)

Sys.time() - aa
save(bikes_bart, file = "data-raw/bikesharing-tempdata/bikes_bart.RData")
save(bikes_sv, file = "data-raw/bikesharing-tempdata/bikes_sv.RData")
save(bikes_reg, file = "data-raw/bikesharing-tempdata/bikes_reg.RData")


############################################################
### COMBINE AGENT PREDICTIONS IN ONE DATA FRAME          ###
############################################################

# Set the "sandy day" lpdens to 0
load("data-raw/bikesharing-tempdata/bikes_bart.RData")
load("data-raw/bikesharing-tempdata/bikes_sv.RData")
load("data-raw/bikesharing-tempdata/bikes_reg.RData")
bikes_atom <- rbind(bikes_bart, bikes_sv, bikes_reg)
bikes_atom[bikes_atom$t == 667, "lpdens"] <- 0
save(bikes_atom, file = "data/bikes_atom.RData")