Skip to content

Fix heading level for birthdays example #21

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
damonbayer wants to merge 1 commit into
base: main
Choose a base branch
from
+9 −9
Open

Fix heading level for birthdays example #21

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
18 changes: 9 additions & 9 deletions birthdays/birthdays.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -499,7 +499,7 @@ sm <- summarise_draws(subset(draws1b, variable = variables))
make_pth_vs_fit(sm, sp)

#'
#' ### Model 2: Slow trend + yearly seasonal trend
#' ## Model 2: Slow trend + yearly seasonal trend
#'
#' The model 2 adds yearly seasonal trend using GP with periodic
#' covariance function.
Expand Down Expand Up @@ -635,7 +635,7 @@ sp <- summarise_draws(subset(pdraws2, variable = variables))
sm <- summarise_draws(subset(draws2, variable = variables))
make_pth_vs_fit(sm, sp)

#' ### Model 3: Slow trend + yearly seasonal trend + day of week
#' ## Model 3: Slow trend + yearly seasonal trend + day of week
#'
#' Based on the quick plotting of the data above, day of week has a
#' clear effect and there are less babies born on Saturday and
Expand Down Expand Up @@ -757,7 +757,7 @@ sp <- summarise_draws(subset(pdraws3, variable = variables))
sm <- summarise_draws(subset(draws3, variable = variables))
make_pth_vs_fit(sm, sp)

#' ### Model 4: long term smooth + seasonal + weekday with increasing magnitude
#' ## Model 4: long term smooth + seasonal + weekday with increasing magnitude
#'
#' Looking at the time series of whole data we see the dots
#' representing the daily values forming three branches that are
Expand Down Expand Up @@ -892,7 +892,7 @@ sp <- summarise_draws(subset(pdraws4, variable = variables))
sm <- summarise_draws(subset(draws4, variable = variables))
make_pth_vs_fit(sm, sp)

#' ### Model 5: long term smooth + seasonal + weekday with time dependent magnitude + day of year RHS
#' ## Model 5: long term smooth + seasonal + weekday with time dependent magnitude + day of year RHS
#'
#' The next component to add is day of year effect. Many bank holidays
#' are every year on the same day of year and there might be also
Expand Down Expand Up @@ -1122,7 +1122,7 @@ sp <- summarise_draws(subset(pth5$draws(), variable = variables))
sm <- summarise_draws(subset(draws5, variable = variables))
make_pth_vs_fit(sm, sp)

#' ### Model 6: long term smooth + seasonal + weekday + day of year
#' ## Model 6: long term smooth + seasonal + weekday + day of year
#'
#' To simplify the analysis of the day of year effect and make the
#' inference during the exploration faster, we drop the time dependent
Expand Down Expand Up @@ -1338,7 +1338,7 @@ f13r <- birthdays |>
#' `r round(mean(1-rvar_mean(f13r$ydist)),3)*100`% fewer births.
#'

#' ### Model 7: long term smooth + seasonal + weekday + day of year normal + floating special days
#' ## Model 7: long term smooth + seasonal + weekday + day of year normal + floating special days
#'
#' We can see in the model 6 results that day of year effects have
#' some dips in the relative number of births that are spread over a
Expand Down Expand Up @@ -1540,7 +1540,7 @@ f13r <- birthdays |>
#' `r round(mean(1-rvar_mean(f13r$ydist)),3)*100`% fewer births.
#'

#' ### Model 8: long term smooth + seasonal + weekday with time dependent magnitude + day of year + special
#' ## Model 8: long term smooth + seasonal + weekday with time dependent magnitude + day of year + special
#'
#' As the day of year and floating day effects work well, we'll add
#' the time dependent day of week effect back to the model.
Expand Down Expand Up @@ -1710,7 +1710,7 @@ sm <- summarise_draws(subset(draws8, variable = variables))
make_pth_vs_fit(sm, sp)

#'
#' ### Model 8+t_nu: day of year effect with Student's t prior
#' ## Model 8+t_nu: day of year effect with Student's t prior
#'
#' Compile Stan model 8 + t_nu [gpbf8tnu.stan](https://github.com/avehtari/Bayesian-Workflow/blob/master/birthdays/gpbf8tnu.stan)
#| label: model8tnu
Expand Down Expand Up @@ -1829,7 +1829,7 @@ sp <- summarise_draws(subset(pth8tnu$draws(), variable = variables))
sm <- summarise_draws(subset(draws8tnu, variable = variables))
make_pth_vs_fit(sm, sp)

#' ### Model 8+RHS: day of year effect with RHS prior
#' ## Model 8+RHS: day of year effect with RHS prior
#'
#' Model 5 had RHS prior but the problem was that optimization result
#' wasn't even close to sensible and MCMC was very slow. Given the
Expand Down