Jake Bowers jwbowers

## gist:c985bd71a3ec90b4e3d7e82595a8d6db
\newcommand{\pfn}{\text{pfn}}
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\newcommand{\bz}{\bm{z}}
\newcommand{\bOne}{\bm{1}}

## commit_sizes.py
import subprocess
import sys
import csv
# uses the GitPython module
import git
from pathlib import Path

## Notice need to install GitPython
## Recommended:
## python3 -m venv .venv

## anytreatment_vs_control.R
library(tidyverse)
library(DeclareDesign)

my_designer <- function(N, tau1, tau2) {
  # N - total sample size
  # tau1 - difference in proportion control versus "easy" message
  # tau2 - difference in proportion control versus "right" message

  fixed_pop <- fabricate(
    N = N,

## lm_lin_example.R

library(estimatr)
library(fabricatr)
library(DeclareDesign)

set.seed(12345)
dat <-
    fabricate(
        N = 50,
        x = sample(1:40, N, replace = TRUE),

## pop_sampling_dd1.R
## We have many group homes (like nursing homes) and few COVID tests.
## We want to test a simple random sample within each home to estimate the proportion positive with COVID (or detect
## any COVID)

## So, each place has a fixed population (N) and we will sample from this place.
## The population should be unique for combination of N and covidprob (which is just .02 in the simulations, but higher for
## testing here).

## I'd like to use the nice DeclareDesign functions like redesign() to assess
## power to detect COVID given different proportions of sampling for each population size

## pop_sampling_dd1.R
> pop <- declare_population(N,
+                           covid=rbinom(N,size=1,prob=covidprob))
> samp  <- declare_sampling(n=n)
> sim_design <-  pop + samp
> set.seed(12345)
> tmp <- draw_data(redesign(sim_design,N=40,n=20,covidprob=.5))
> table(tmp$covid)

 0  1
 9 11

## Basic test for Declare Design
#' Wrap a basic one-way t-test so that it's usable by the tidy functions
#' @param data The data that we pass in. See discussion of tidy_estimator and handlers in declare_estimator
#' @returns A data frame that contains quantities useful for assessing the design and estimator
our_ttest <- function(data) {
    require(coin)
  res <- coin::oneway_test(
    outcome ~ factor(Xclus),
    data = data,
    distribution = "asymptotic"
  )

## mwe_noncompliance_design.R
## Example of simulating data from a design with one-sided non-compliance

types <- c("Always-Taker", "Never-Taker", "Complier", "Defier")
direct_effect_of_encouragement <- 0.0
proportion_defiers <- 0.0
design <- declare_population(
    N = 500,
    type = sample( types, N, replace = TRUE,
      prob = c(0.1, 0.1, 0.8 - proportion_defiers, proportion_defiers)
    ),

## ebp-cartoon2.html
<!DOCTYPE html>
<meta charset="utf-8">
<!-- build:css v/cmx.css -->
<link rel="stylesheet" href="styles/cmx.css">
<link rel="stylesheet" href="styles/cmx-overlay.css">
<!-- endbuild -->
<!-- build:js v/cmx.js -->
<script data-main="scripts/main" src="scripts/vendor/require.js"></script>
<!-- endbuild -->
<style>.cmx-user-scene4 .cmx-text-border .cmx-path {stroke: orange}</style>

## ebp-cartoon-weak-theory.html
<!DOCTYPE html>
<meta charset="utf-8">
<!-- build:css v/cmx.css -->
<link rel="stylesheet" href="http://cmx.io/v/0.1/cmx.css"/>
<script src="http://cmx.io/v/0.1/cmx.js"></script>
<!-- <link rel="stylesheet" href="styles/cmx-overlay.css"> -->
<!-- endbuild -->
<!-- build:js v/cmx.js -->
<!-- <script data-main="scripts/main" src="scripts/vendor/require.js"></script> -->
<!-- endbuild -->
	\newcommand{\pfn}{\text{pfn}}
	\newcommand{\bset}{\mathcal{B}}
	\newcommand{\splitfn}{\text{splitfn}}
	\newcommand{\bZ}{\bm{Z}}
	\newcommand{\bS}{\bm{S}}
	\newcommand{\bz}{\bm{z}}
	\newcommand{\bOne}{\bm{1}}
	import subprocess
	import sys
	import csv
	# uses the GitPython module
	import git
	from pathlib import Path

	## Notice need to install GitPython
	## Recommended:
	## python3 -m venv .venv
	library(tidyverse)
	library(DeclareDesign)

	my_designer <- function(N, tau1, tau2) {
	# N - total sample size
	# tau1 - difference in proportion control versus "easy" message
	# tau2 - difference in proportion control versus "right" message

	fixed_pop <- fabricate(
	N = N,

	library(estimatr)
	library(fabricatr)
	library(DeclareDesign)

	set.seed(12345)
	dat <-
	fabricate(
	N = 50,
	x = sample(1:40, N, replace = TRUE),
	## We have many group homes (like nursing homes) and few COVID tests.
	## We want to test a simple random sample within each home to estimate the proportion positive with COVID (or detect
	## any COVID)

	## So, each place has a fixed population (N) and we will sample from this place.
	## The population should be unique for combination of N and covidprob (which is just .02 in the simulations, but higher for
	## testing here).

	## I'd like to use the nice DeclareDesign functions like redesign() to assess
	## power to detect COVID given different proportions of sampling for each population size
	> pop <- declare_population(N,
	+ covid=rbinom(N,size=1,prob=covidprob))
	> samp <- declare_sampling(n=n)
	> sim_design <- pop + samp
	> set.seed(12345)
	> tmp <- draw_data(redesign(sim_design,N=40,n=20,covidprob=.5))
	> table(tmp$covid)

	0 1
	9 11
	#' Wrap a basic one-way t-test so that it's usable by the tidy functions
	#' @param data The data that we pass in. See discussion of tidy_estimator and handlers in declare_estimator
	#' @returns A data frame that contains quantities useful for assessing the design and estimator
	our_ttest <- function(data) {
	require(coin)
	res <- coin::oneway_test(
	outcome ~ factor(Xclus),
	data = data,
	distribution = "asymptotic"
	)
	## Example of simulating data from a design with one-sided non-compliance

	types <- c("Always-Taker", "Never-Taker", "Complier", "Defier")
	direct_effect_of_encouragement <- 0.0
	proportion_defiers <- 0.0
	design <- declare_population(
	N = 500,
	type = sample( types, N, replace = TRUE,
	prob = c(0.1, 0.1, 0.8 - proportion_defiers, proportion_defiers)
	),
	<!DOCTYPE html>
	<meta charset="utf-8">
	<!-- build:css v/cmx.css -->
	<link rel="stylesheet" href="styles/cmx.css">
	<link rel="stylesheet" href="styles/cmx-overlay.css">
	<!-- endbuild -->
	<!-- build:js v/cmx.js -->
	<script data-main="scripts/main" src="scripts/vendor/require.js"></script>
	<!-- endbuild -->
	<style>.cmx-user-scene4 .cmx-text-border .cmx-path {stroke: orange}</style>