benwhalley

benwhalley

benwhalley

library(rstanarm)
library(tidybayes)
m1 <- stan_lmer(t2_accuracy ~ lag*condition + (1|p), data = rsvp_accuracy)

nd <- expand.grid(condition=unique(rsvp_accuracy$condition), lag=factor(c(1,3))) %>% as.tibble() %>% mutate(p=Inf)

m1.preds <- add_fitted_samples(nd, m1)
m1.preds.wide <- m1.preds %>% 
  data.table::dcast(.iteration+condition~lag) 

benwhalley

<?xml version="1.0" ?>
 <auth>
     <publications>
         <publication>
             <authors>bjwhalley,thollins</authors>
             <elements_id>1</elements_id>
             <bibtex>{Stuff here}</bibtex>
             <timestamp>2050-01-31T20:00-4:00</timestamp>
         </publication>
         <publication>

benwhalley

sprintcol <- function(x, sig.digits = 3) {
  rounded <- paste(signif(x, sig.digits))
  max.decimal <-
    str_extract(rounded, "\\.(\\d+)") %>%
    str_length() %>%
    max(c(0, .), na.rm = T) - 1
  max.decimal <- max(max.decimal, min(abs(max.decimal), 0))
  sprintf(fmt = glue::glue("%.{max.decimal}f"), x)
}

benwhalley

benwhalley

---
title: "Untitled"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

benwhalley

---
title: Quantifying uncertainty
output: html_document
---

```{r}
library(tidyverse)
```

benwhalley

Prior <- c(set_prior("normal(0, 30)", class = "b"))

 options(mc.cores=parallel::detectCores ()-1)


f.interaction <- bf(Pressure ~ Awareness * 
    Agency * 
    Resp.Alt * 
    VisualPrime * 

benwhalley

library(brms)
library(tidyverse)

m1 <- rstanarm::stan_glm(mpg~wt, data=mtcars)
prior_summary(m1)

# see what priors brms will let you set
get_prior(mpg~wt, data=mtcars)
# then then mimic beta priors from rstanarm (inc rstanarm autoscaling)
m2 <- brm(mpg~wt, data=mtcars, sample_prior="only",