Main data simulation function
require(DrBats)
## Le chargement a nécessité le package : DrBats
## Le chargement a nécessité le package : rstan
## Le chargement a nécessité le package : StanHeaders
## Le chargement a nécessité le package : ggplot2
## rstan (Version 2.21.3, GitRev: 2e1f913d3ca3)
## For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores()).
## To avoid recompilation of unchanged Stan programs, we recommend calling
## rstan_options(auto_write = TRUE)
## Do not specify '-march=native' in 'LOCAL_CPPFLAGS' or a Makevars file
<- drbats.simul(N = 10,
st_data t.range = c(0, 1000),
b.range = c(0.2, 0.4),
c.range = c(0.6, 0.8),
b.sd = 0.5,
c.sd = 0.5,
y.range = c(-5, 5),
sigma2 = 0.2,
breaks = 15,
data.type = 'sparse.tend')
<-c("#ee204d", "#1f75fe", "#1cac78", "#ff7538", "#b4674d", "#926eae",
mycol"#fce883", "#000000", "#78dbe2", "#6e5160", "#ff43a4")
The parameters b.range
and c.range
dictate the location of two peaks, and b.sd
and c.sd
the variance of the peaks. Once the signals have been simulated, the function samples observation times over the range of possible times t.range
. Few times are chosen in b.range
and c.range
, and many are chosen outside these ranges.
The parameter data.type
specifies the type of signal to simulate: sparse
will simulate a bi-modal signal that is flat between the modes. The sparse.tend
option will simulate bi-modal signals with a trend, and the sparse.tend.cos
will simulate periodic bi-modal signals with a trend.
matplot(t(st_data$t), t(st_data$X), type = 'l', lty = 1, lwd = 1,
xlab = 'Time', ylab = ' ', col = mycol[1:10])
points(t(st_data$t), t(st_data$X), pch = '.')