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library(xkcd) | |
library(tidyverse) | |
d <- tibble(age = 1:20) |> | |
mutate(upper = 1e6 * age * 12 + ifelse(age > 5, 2.5e5 * 52 * (age - 5), 0), | |
lower = 1e5 * age * 12) |> | |
pivot_longer(upper:lower, names_to = "bound", values_to = "vocabulary") | |
pdf("~/Projects/AI commentaries/scale.pdf", width = 5, height = 4) | |
ggplot(d, aes(x = age, y = vocabulary, col = bound)) + |
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library(wordbankr) | |
library(langcog) | |
library(tidyverse) | |
library(brms) | |
library(forcats) | |
library(survey) | |
library(gamlss) | |
theme_set(theme_mikabr()) | |
font <- theme_mikabr()$text$family |
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# relies on zoo package | |
# function to find the baseline value | |
baseline_looking <- function (lts) { | |
# select lts > 12s | |
lts_12 <- lts[lts > 12 & !is.na(lts)] | |
# sum the first three | |
baseline <- sum(lts_12[1:3], na.rm=TRUE) | |
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library(tidyverse) | |
library(assertthat) | |
shuffle_data <- function(d) { | |
d$participant_id <- shuffle(d$participant_id) | |
return(d) | |
} | |
get_knower_level <- function (q, r, technique = "perfect") { |
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## this little script shows off the use of the pubmed API through bioconductor | |
## requires installing Biopython (using pip) | |
## also requires installing the DTD files for each of the Entrez API calls, | |
## but the instructions for this are given when you run the script | |
## useful list of Entrez databases that can be queried through API | |
# pmc_pubmed PubMed citations for these articles | |
# pmc_refs_pubmed PubMed article citing PMC article | |
# pmc_pmc_cites PMC articles that given PMC article cites | |
# pmc_pmc_citedby PMC article citing given PMC article |
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# approach from | |
# https://www.medrxiv.org/content/10.1101/2022.11.02.22281762v1.full.pdf | |
library(tidyverse) | |
library(survival) | |
library(ggsurvfit) | |
#devtools::install_github("maxlinde/baymedr") | |
library(baymedr) | |
# library(BayesSurvival) |
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--- | |
title: "mtcars example markdown" | |
author: "Mike Frank" | |
date: "2023-03-21" | |
output: | |
html_document: | |
toc: true | |
toc_float: true | |
--- |
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library(tidyverse) | |
library(lme4) | |
sgf <- read_csv("https://raw.githubusercontent.com/langcog/experimentology/main/data/tidyverse/stiller_scales_data.csv") |> | |
mutate(age_group = cut(age, 2:5, include.lowest = TRUE), | |
condition_f = factor(ifelse(condition == "Label", | |
"Experimental", "Control")), | |
age_centered = age - mean(age)) | |
mod1 <- glmer(correct ~ age * condition + (1|subid) + (1|item), |
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# starts at line 716 of paper.Rmd | |
d_lmer_scale <- d %>% | |
filter(trial_type != "train") %>% | |
mutate(log_lt = log(looking_time), | |
age_mo = scale(age_mo, scale = FALSE), | |
trial_num = trial_num - 8.5, | |
item = paste0(stimulus_num, trial_type)) %>% | |
filter(!is.na(log_lt), !is.infinite(log_lt)) |
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library(tidyverse) | |
library(gamlss) | |
d <- readxl::read_excel("sample_data.xlsx") | |
# model | |
max_vocab <- max(d$`Vocabulary production`) | |
# transformation to 0-1 for beta model | |
# note that beta data cannot be exactly 0 or 1, it may be necessary to add/subtract .001 for data including 0s and 1s |
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