options(width=200)
library(tidyverse)
gpt <- rio::import("~/Downloads/tmp3.csv")
items <- tibble(var=c(str_c("ipipc", 1:10), str_c("grit", 1:10)),
label = names(gpt)[-1])
knitr::kable(items)
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## dumbed-down simulation of selection bias with noise (aka selecting on fielding of scale before fielding) | |
# set seed for reproducibility | |
set.seed(123) | |
# parameters | |
n_studies <- 100000 | |
noise_dist <- seq(0.01, 0.07, 0.01) | |
## draw distribution of true alpha values |
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library(tidyverse) | |
# simulate unobserved trait and items with error | |
N <- 100 | |
dat <- tibble( | |
id = 1:N, | |
trait = rnorm(N), | |
item1 = trait + rnorm(N), | |
item2 = trait + rnorm(N), | |
item3 = trait + rnorm(N), | |
item4 = trait + rnorm(N), |
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library(dplyr) | |
N = 10000 | |
people <- tibble( | |
male = sample(0:1, N, TRUE), | |
trait = rnorm(N) + male) | |
true_means <- people %>% | |
group_by(male) %>% | |
summarise(sex_mean = mean(trait)) | |
people <- people %>% left_join(true_means) %>% | |
left_join(true_means %>% rename(opposite_sex_mean = sex_mean) %>% mutate(male = 1-male)) %>% |
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N <- 5000 | |
sim <- tibble( | |
group = rep(0:1, each = N/2), | |
evb_strength = 0.1 + group * 0.9, | |
# four orthogonal traits | |
trait1 = rnorm(N), | |
trait2 = rnorm(N), | |
trait3 = rnorm(N), | |
trait4 = rnorm(N), | |
# evaluative bias |
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n <- 1000 | |
people <- tibble( | |
children = rpois(n, 1.4), | |
# for childless people, sat with childcare is not applicable | |
sat_with_child_care = if_else(children > 0, rnorm(n), NA_real_), | |
sat_with_housing = rnorm(n), | |
happiness = rnorm(n) + sat_with_housing + | |
if_else(children > 0, sat_with_child_care, 0), | |
) |
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mean_se_cluster <- function (x, mult = 1, cluster = NULL) | |
{ | |
x_na <- is.na(x) | |
x <- x[!x_na] | |
cluster <- cluster[!x_na] | |
stopifnot(!is.null(cluster)) | |
mod <- lme4::lmer(x ~ 1 + (1 | cluster)) | |
intercept <- broom.mixed::tidy(mod, effects = "fixed") | |
se <- mult * intercept$std.error | |
mean <- intercept$estimate |
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library(tidyverse) | |
n_papers <- 1000 | |
papers <- tibble( | |
paper = 1:n_papers, | |
quality = rnorm(n_papers), | |
reviews = 0, | |
published = FALSE, | |
journal = NA_real_, | |
most_recent_assessment = NA_real_ |
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library(brms) | |
library(dplyr) | |
n = 1000 | |
fakedata = data_frame( | |
# structure | |
iv = rnorm(n), | |
dv = 0.5 * iv + 0.5 * rnorm(n), | |
# measurement | |
iv_m = iv + 1 * rnorm(n), | |
dv_m = dv + 1 * rnorm(n) |
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# Manual page | |
library(curl) | |
?multi | |
# Example of 100 requests over 10 connections | |
real = vector("list", 10) | |
for(i in 1:10){ | |
h <- new_handle(url = "https://httpbin.org/get") | |
multi_add(h, complete = function(res){ |
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