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https://github.com/mvuorre/brmstools

Helper functions for brmsfit objects (DEPRECATED)
https://github.com/mvuorre/brmstools

bayesian-inference brms r-package

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Helper functions for brmsfit objects (DEPRECATED)

Lists

README 

        
---

output: github_document

---



```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# brmstools



brmstools was an R package that provided one-liners for drawing figures from regression models fitted with the [brms package](https://github.com/paul-buerkner/brms).



**The package is no longer maintained**: Its functionality can be replicated by using native functions from the brms package and functions from the [tidybayes package](https://github.com/mjskay/tidybayes). Example code for reproducing brmstools' figures is shown below.



# Required packages



```{r}

library(brms)

library(tidyverse)

library(tidybayes)  # https://github.com/mjskay/tidybayes

library(ggridges)  # https://github.com/clauswilke/ggridges

```



# Forest plots



After fitting a meta-analytic model, creating a forest plot is easy with tidybayes and ggridges.



First, fit a meta-analytic model with some example data from the [metafor](https://github.com/wviechtb/metafor) package.



```{r}

data("dat.bangertdrowns2004", package = "metafor")

dat <- dat.bangertdrowns2004 %>%

  mutate(study = paste0(author, " (", year, ")"), sei = sqrt(vi)) %>%

  select(study, yi, sei) %>%

  slice(1:15)

dat$study <- str_replace(dat$study, ",", "")  # Remove commas in study names

fit_rem <- brm(

  yi | se(sei) ~ 1 + (1|study),

  data = dat,

  cores = 4, 

  control=list(adapt_delta = .99),

  file = "fit-rem"

)

```



For an explanation of `tidybayes::spread_draws()`, refer to .



```{r}

# Study-specific effects are deviations + average

out_r <- spread_draws(fit_rem, r_study[study,term], b_Intercept) %>% 

  mutate(b_Intercept = r_study + b_Intercept) 

# Average effect

out_f <- spread_draws(fit_rem, b_Intercept) %>% 

  mutate(study = "Average")

# Combine average and study-specific effects' data frames

out_all <- bind_rows(out_r, out_f) %>% 

  ungroup() %>%

  # Ensure that Average effect is on the bottom of the forest plot

  mutate(study = fct_relevel(study, "Average"))

# Data frame of summary numbers

out_all_sum <- group_by(out_all, study) %>% 

      mean_qi(b_Intercept)

# Draw plot

out_all %>%   

  ggplot(aes(b_Intercept, study)) +

  geom_density_ridges(

    rel_min_height = 0.01, 

    col = NA,

    scale = 1

  ) +

  geom_pointintervalh(

    data = out_all_sum, size = 1

  ) +

  geom_text(

    data = mutate_if(out_all_sum, is.numeric, round, 2),

    # Use glue package to combine strings

    aes(label = glue::glue("{b_Intercept} [{.lower}, {.upper}]"), x = Inf),

    hjust = "inward"

  )

```



# Panel plots



First, fit a multilevel model to multiple subjects' data



```{r}

data(sleepstudy, package = "lme4")

fit_ml <- brm(

  Reaction ~ Days + (Days|Subject),

  data = sleepstudy,

  cores = 4,

  file = "fit-ml"

)

```



Draw a panel plot using `brms::marginal_effects()`



```{r}

marginal_effects(

  fit_ml,

  "Days", 

  conditions = distinct(sleepstudy, Subject),

  re_formula = NULL

  )

```



# Spaghetti plots



Spaghetti plot combines `marginal_effects()` of the random and fixed effects' regression lines.



```{r}

out_f <- marginal_effects(

  fit_ml,

  "Days"

  )[[1]]

out_r <- marginal_effects(

  fit_ml,

  "Days", 

  conditions = distinct(sleepstudy, Subject),

  re_formula = NULL

  )[[1]]

out_r %>% 

  ggplot(aes(Days, estimate__)) +

  geom_ribbon(

    data = out_f,

    aes(ymin = lower__, ymax = upper__),

    alpha = .33

  ) +

  geom_line(data = out_f, size = 2) +

  geom_line(aes(group = Subject))

```