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BRMS tutorial: add rstan example #346

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Dec 6, 2024
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BRMS tutorial: add rstan example #346

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d09e99e
brms_rstan: general updates
falkmielke Nov 21, 2024
7b170ac
brms_rstan: poisson model, plain+hierarchical
falkmielke Nov 21, 2024
bfa0a19
brms_rstan: windows testing, index, author
falkmielke Nov 22, 2024
d3a0159
brms-rstan: wording
falkmielke Nov 26, 2024
e17c1a8
brms-rstan: wording (nl)
falkmielke Nov 26, 2024
b487a1e
brms-rstan: wording (nl)
falkmielke Nov 26, 2024
fc2f757
brms-rstan: wording (nl)
falkmielke Nov 26, 2024
194ee75
brms-rstan: wording (nl)
falkmielke Nov 26, 2024
bb32e0d
brms-rstan: rstan moved as suggested.
falkmielke Nov 26, 2024
0d113dd
brms-rstan: generalizing frequentists; translation
falkmielke Nov 26, 2024
58dc8af
brms-rstan: generalizing frequentists (en)
falkmielke Nov 26, 2024
4740347
brms-rstan: mentioning cmdstanr backend
falkmielke Nov 26, 2024
4f731a9
brms-rstan: re-organize static files
falkmielke Nov 26, 2024
75efd94
brms-rstan: knit files; image issues
falkmielke Nov 26, 2024
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4 changes: 4 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -62,3 +62,7 @@ content/**/*.html

# Local folders with binary data
data_gisclub/

# png images for the brms tutorial
/content/tutorials/r_brms/brms_eng/*.png
/content/tutorials/r_brms/brms_nl/*.png
16 changes: 8 additions & 8 deletions content/tutorials/r_brms/brms_eng/bayesian_statistics_1_script_eng.R
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Expand Up @@ -48,7 +48,7 @@ confint(lm1, level = 0.9)


## -------------------------------------------------------------------------------------------------------------------------------------
source(file = "./source/mcmc_functions.R")
source(file = here("code", "brms_modeling", "mcmc_functions.R"))


## -------------------------------------------------------------------------------------------------------------------------------------
Expand Down Expand Up @@ -230,7 +230,7 @@ fit_normal1 <- brm(
family = gaussian(), # we use the Normal distribution
data = ants_df, # specify data
chains = nchains, # MCMC parameters
warmup = burnin,
warmup = burnin,
iter = niter,
cores = nparallel,
thin = thinning,
Expand Down Expand Up @@ -335,7 +335,7 @@ mcmc_neff(ratios_fit_normal1) + yaxis_text(hjust = 1)

## ----simple-model-fit1----------------------------------------------------------------------------------------------------------------
# Visualise model fit via bayesplot package
pp_check(fit_normal1, type = "dens_overlay_grouped", ndraws = 100,
pp_check(fit_normal1, type = "dens_overlay_grouped", ndraws = 100,
group = "habitat")


Expand All @@ -346,7 +346,7 @@ fit_poisson1 <- brm(
family = poisson(), # we use the Poisson distribution
data = ants_df, # specify the data
chains = nchains, # MCMC parameters
warmup = burnin,
warmup = burnin,
iter = niter,
cores = nparallel,
thin = thinning,
Expand All @@ -366,7 +366,7 @@ mcmc_rhat(rhats_fit_poisson1) + yaxis_text(hjust = 1)

## ----poisson-model-fit-vis------------------------------------------------------------------------------------------------------------
# Visualise model fit via bayesplot package
pp_check(fit_poisson1, type = "dens_overlay_grouped", ndraws = 100,
pp_check(fit_poisson1, type = "dens_overlay_grouped", ndraws = 100,
group = "habitat")


Expand All @@ -377,7 +377,7 @@ fit_poisson2 <- brm(
family = poisson(),
data = ants_df,
chains = nchains,
warmup = burnin,
warmup = burnin,
iter = niter,
cores = nparallel,
thin = thinning,
Expand All @@ -399,7 +399,7 @@ mcmc_rhat(rhats_fit_poisson2) + yaxis_text(hjust = 1)
## ----rand-intercept-model-fit-vis-----------------------------------------------------------------------------------------------------
# Visualise model fit of the Poisson model with random intercept via
# bayesplot package
pp_check(fit_poisson2, type = "dens_overlay_grouped", ndraws = 100,
pp_check(fit_poisson2, type = "dens_overlay_grouped", ndraws = 100,
group = "habitat")


Expand Down Expand Up @@ -518,7 +518,7 @@ fit_poisson2 %>%
# calculate average numbers and convert to long format for visualisation
mutate(bog = exp(b_Intercept),
forest = exp(b_Intercept + b_habitatForest)) %>%
pivot_longer(cols = c("bog", "forest"), names_to = "habitat",
pivot_longer(cols = c("bog", "forest"), names_to = "habitat",
values_to = "sp_rich") %>%
# visualise via ggplot()
ggplot(aes(y = sp_rich, x = habitat)) +
Expand Down
238 changes: 214 additions & 24 deletions content/tutorials/r_brms/brms_eng/workshop_1_mcmc_en_brms_eng.Rmd
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18 changes: 9 additions & 9 deletions content/tutorials/r_brms/brms_nl/bayesian_statistics_1_script.R
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Expand Up @@ -48,7 +48,7 @@ confint(lm1, level = 0.9)


## -------------------------------------------------------------------------------------------------------------------------------------
source(file = "./source/mcmc_functions.R")
source(file = here::here("code", "brms_modeling", "mcmc_functions.R"))


## -------------------------------------------------------------------------------------------------------------------------------------
Expand Down Expand Up @@ -232,7 +232,7 @@ fit_normal1 <- brm(
family = gaussian(), # we gebruiken de Normaal verdeling
data = ants_df, # ingeven data
chains = nchains, # MCMC parameters
warmup = burnin,
warmup = burnin,
iter = niter,
cores = nparallel,
thin = thinning,
Expand Down Expand Up @@ -298,7 +298,7 @@ as_draws_df(fit_normal1, variable = parameters) %>%


## ----simpel-model-posterior-density2--------------------------------------------------------------------------------------------------
# Visualisatie density plot van de posterior voor ieder van de chains apart in
# Visualisatie density plot van de posterior voor ieder van de chains apart in
# overlay. via Bayesplot package
mcmc_dens_overlay(fit_normal1, pars = parameters)

Expand Down Expand Up @@ -337,7 +337,7 @@ mcmc_neff(ratios_fit_normal1) + yaxis_text(hjust = 1)

## ----simpel-model-fit1----------------------------------------------------------------------------------------------------------------
# Visualiseer model fit via Bayesplot package
pp_check(fit_normal1, type = "dens_overlay_grouped", ndraws = 100,
pp_check(fit_normal1, type = "dens_overlay_grouped", ndraws = 100,
group = "habitat")


Expand All @@ -348,7 +348,7 @@ fit_poisson1 <- brm(
family = poisson(), # we gebruiken de Poisson verdeling
data = ants_df, # ingeven data
chains = nchains, # MCMC parameters
warmup = burnin,
warmup = burnin,
iter = niter,
cores = nparallel,
thin = thinning,
Expand All @@ -368,7 +368,7 @@ mcmc_rhat(rhats_fit_poisson1) + yaxis_text(hjust = 1)

## ----poisson-model-fit-vis------------------------------------------------------------------------------------------------------------
# Visualiseer model fit via Bayesplot package
pp_check(fit_poisson1, type = "dens_overlay_grouped", ndraws = 100,
pp_check(fit_poisson1, type = "dens_overlay_grouped", ndraws = 100,
group = "habitat")


Expand All @@ -379,7 +379,7 @@ fit_poisson2 <- brm(
family = poisson(),
data = ants_df,
chains = nchains,
warmup = burnin,
warmup = burnin,
iter = niter,
cores = nparallel,
thin = thinning,
Expand All @@ -401,7 +401,7 @@ mcmc_rhat(rhats_fit_poisson2) + yaxis_text(hjust = 1)
## ----rand-intercept-model-fit-vis-----------------------------------------------------------------------------------------------------
# Visualiseer model fit van het Poisson model met random intercept via
# Bayesplot package
pp_check(fit_poisson2, type = "dens_overlay_grouped", ndraws = 100,
pp_check(fit_poisson2, type = "dens_overlay_grouped", ndraws = 100,
group = "habitat")


Expand Down Expand Up @@ -520,7 +520,7 @@ fit_poisson2 %>%
# bereken gemiddelde aantallen en zet om naar lang formaat voor visualisatie
mutate(bog = exp(b_Intercept),
forest = exp(b_Intercept + b_habitatForest)) %>%
pivot_longer(cols = c("bog", "forest"), names_to = "habitat",
pivot_longer(cols = c("bog", "forest"), names_to = "habitat",
values_to = "sp_rich") %>%
# visualiseer via ggplot()
ggplot(aes(y = sp_rich, x = habitat)) +
Expand Down
7 changes: 0 additions & 7 deletions content/tutorials/r_brms/brms_nl/install_packages.R
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93 changes: 0 additions & 93 deletions content/tutorials/r_brms/brms_nl/mcmc_functions.R
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