mcfrank

library(wordbankr)
library(tidyverse)

possess_data <- get_instrument_data(language = "English (American)", 
                                    form = "WS", 
                                    items = "item_687") # note that 687 is the s-possess item
admin_data <- get_administration_data(language = "English (American)", 
                                      form = "WS")

left_join(possess_data, admin_data) %>% 

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library(tidyverse) 
n_sim <- 100
sims <- expand_grid(n_total = seq(50,500,25), 
                    i = 1:n_sim) %>%
  mutate(idx = 1:n()) %>%
  split(.$idx) %>%
  map_df(function (df) {
    cntl_sim <- tibble(choice = c(rbinom(n = df$n_total/2, size = 1, p = .68), 
                                  rbinom(n = df$n_total/2, size = 1, p = .5)), 
                       condition = c(rep("social", df$n_total/2), 

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library(MASS)
library(tidyverse)
# from https://homeweb.unifr.ch/VanhoveJ/Pub/simulation_covariates.html

# Define function
# n <- 20   # observations per group
# diff <- 0 # difference between groups
# sd_y <- 3 # within-group sd of outcome (population-level)
# rho_covars <- c(0.7, 0.3, 0) # correlation of control variables with outcome
# in control group; add more coefficients to

mcfrank

library(tidyverse)
library(brms)
library(langcog)

d <- tibble(subject = c(1, 1, 1, 1, 2, 2, 2, 2,
                        3, 3, 3, 3, 4, 4, 4, 4), 
            response = c("E",NA,"A","error", 
                         "E","E","E","error",
                         "A","A","E","error",
                         "A","A","A","A"), 

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# based on http://egap.org/methods-guides/10-things-know-about-covariate-adjustment
library(MASS)  # for mvrnorm()
library(tidyverse)

set.seed(1234567)
num.reps = 1000

# True treatment effect is 0 for every unit

adj.est = function(n, cov.matrix, treated) {

mcfrank

iqs <- data_frame(iq = rnorm(mean = 100, sd = 15, n= 40), 
                  school = c(rep("Stanford",20), rep("Berkeley",20)), 
                  year = factor(rep(c(1950,1990),20)))
                  
summary(lm(iq ~ school * year, data = iqs))

summary(lm(scale(iq) ~ school * year, data = iqs))
                  

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library(brms)
library(lme4)
library(here)
library(tidyverse)

d <- read_csv(here("processed_data/03_data_trial_main.csv")) %>%
  mutate(method = case_when(
    method == "singlescreen" ~ "Central fixation",
    method == "eyetracking" ~ "Eye tracking",
    method == "hpp" ~ "HPP",

mcfrank

library(tidyverse)
library(ggthemes)

n_trials <- c(2,4,8)
p_anticipation <- seq(0,1,.1)
n_subs <- c(12,24,36,48)
n_sims <- 1000

d <- expand.grid(n_trials = n_trials, 
            p_anticipation = p_anticipation, 

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library(tidyverse)

# Nonexistence context, Nonexistence referent, apples? QUD
nna <- tibble(ratio = c("ratio0", "ratio1", "ratio2", "ratio3"),
              probs = c(0.3323976330361966, 0.39954163105573637, 0.37453297805618113, 0.3323976330361966),
              context = "nonexistence context",
              referent = "nonexistence referent", 
              qud = "apples?")

naa <- tibble(ratio = c("ratio0", "ratio1", "ratio2", "ratio3"), 

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library(tidyverse)
foo <- data_frame(time = c(1:5, 1:5),
                  code = c(1,2,3,2,1,0,0,1,2,1), 
                  subid = c(rep(1,5), rep(2, 5)))

foo %>%
  group_by(subid) %>%
  mutate(d_code = c(0,diff(code)),
         one_two_transition = code == 2 & d_code == 1,
         two_three_transition = code == 3 & d_code == 1) %>%