Michael DeWitt medewitt

## ROC Curve for Power.ipynb

      
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                medewitt
                / ROC Curve for Power.ipynb
            
            
              Created
              November 21, 2017 17:51
                — forked from seanjtaylor/ROC Curve for Power.ipynb
            
          
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## Bayesian Survey and Other Neat things
qualititative coding package
https://github.com/ropenscilabs/qcoder

Packages
lavaan, blavaan, blavaan.fimlm, lavaan.survey

panelView: Visualizing Panel Data with Dichotomous Treatments

ggstatsplot: 'ggplot2' Based Plots with Statistical Details
ggplotify: Convert Plot to 'grob' or 'ggplot' Object

## medewitt
https://app.powerbi.com/Redirect?action=OpenApp&appId=0ebba6cb-7c99-4e6e-8dbf-a1aae3066680&ctid=3ce4c247-6019-4439-a614-97d7bdab77f3

https://app.powerbi.com/Redirect?action=OpenApp&appId=0ebba6cb-7c99-4e6e-8dbf-a1aae3066680&ctid=3ce4c247-6019-4439-a614-97d7bdab77f3

## possession_receiver.R
library(tidyverse)

humphries <- tribble(
  ~"Year", ~"G", ~"Rec", ~"Yards", ~"Avg", ~"TD",
  2011, 14, 15, 130, 8.7, 0,
  2012, 13, 41, 280, 6.8, 1,
  2013, 12, 41, 483, 11.8, 2,
  2014, 13, 30, 204, 6.8, 0) %>%
  mutate(player = "Humphries") %>%
  select(Year, Rec, Avg, TD, player)

## exercises_rmarkdown.Rmd
---
title: "Rmarkdown Exercises"
author: "Your Name"
date: "`r Sys.Date()`"
output:
  html_document:
    toc: true
    toc_float: true
    number_sections: true
    df_print: paged

## ggcoef_plot.R
# Coefficient Plot Implemented in ggplot2 From Faraway's Linear Models with R
#'@description Building a Gelman-esque coefplot in ggplot based on the code
#'    in Julian Faraway's Linear Models with R
#'@param fit a lm or glm object
#'@param ci the confidence levels to consider
#'@export

ggcoef_plot <- function(fit, ci = 0.95){
  stopifnot(class(fit) %in% c("lm", "glm"))


## explore-loss.R

# Parameters
set.seed(42)
n <- 100L
#true model

x <- rnorm(n, 5, 1)

treat <- rep(c(0,1), n/2)

## impeachment.csv

          
            date
             approve
             n
             moe
             pollster

            
              2019-09-29
               47
               1009
               3.5
               ssr

            
              2019-05-31
               41
               1009
               3.5
               ssr

            
              2019-04-28
               37
               1009
               3.5
               ssr

            
              2019-03-17
               36
               1009
               3.5
               ssr

            
              2018-12-09
               43
               1009
               3.5
               ssr

            
              2018-09-09
               47
               1009
               3.5
               ssr

            
              2018-06-17
               42
               1009
               3.5
               ssr

            
              2019-09-30
               45
               2234
               2.4
               ipsos

            
              2019-09-30
               47
               1115
               3.6
               Quinnipiac

## dummy_synth.R
library(Synth)
data(synth.data)

synth.data$dummy <- rnorm(168, 0, .00001)

# create matrices from panel data that provide inputs for synth()
dataprep.out<-
  dataprep(
    foo = synth.data,
    predictors = c("dummy"),

## multi-data-sim

library(dplyr)
library(glmnet)
library(furrr)
plan("multisession")


run_simulation <- function(n = 100, num_pred = 50,
                           rho = 0, true_beta= -.1,
                           error = 0.01){
	qualititative coding package
	https://github.com/ropenscilabs/qcoder

	Packages
	lavaan, blavaan, blavaan.fimlm, lavaan.survey

	panelView: Visualizing Panel Data with Dichotomous Treatments

	ggstatsplot: 'ggplot2' Based Plots with Statistical Details
	ggplotify: Convert Plot to 'grob' or 'ggplot' Object
	https://app.powerbi.com/Redirect?action=OpenApp&appId=0ebba6cb-7c99-4e6e-8dbf-a1aae3066680&ctid=3ce4c247-6019-4439-a614-97d7bdab77f3

	https://app.powerbi.com/Redirect?action=OpenApp&appId=0ebba6cb-7c99-4e6e-8dbf-a1aae3066680&ctid=3ce4c247-6019-4439-a614-97d7bdab77f3
	library(tidyverse)

	humphries <- tribble(
	~"Year", ~"G", ~"Rec", ~"Yards", ~"Avg", ~"TD",
	2011, 14, 15, 130, 8.7, 0,
	2012, 13, 41, 280, 6.8, 1,
	2013, 12, 41, 483, 11.8, 2,
	2014, 13, 30, 204, 6.8, 0) %>%
	mutate(player = "Humphries") %>%
	select(Year, Rec, Avg, TD, player)
	---
	title: "Rmarkdown Exercises"
	author: "Your Name"
	date: "`r Sys.Date()`"
	output:
	html_document:
	toc: true
	toc_float: true
	number_sections: true
	df_print: paged
	# Coefficient Plot Implemented in ggplot2 From Faraway's Linear Models with R
	#'@description Building a Gelman-esque coefplot in ggplot based on the code
	#' in Julian Faraway's Linear Models with R
	#'@param fit a lm or glm object
	#'@param ci the confidence levels to consider
	#'@export

	ggcoef_plot <- function(fit, ci = 0.95){
	stopifnot(class(fit) %in% c("lm", "glm"))

	# Parameters
	set.seed(42)
	n <- 100L
	#true model

	x <- rnorm(n, 5, 1)

	treat <- rep(c(0,1), n/2)
date	approve	n	moe	pollster
2019-09-29	47	1009	3.5	ssr
2019-05-31	41	1009	3.5	ssr
2019-04-28	37	1009	3.5	ssr
2019-03-17	36	1009	3.5	ssr
2018-12-09	43	1009	3.5	ssr
2018-09-09	47	1009	3.5	ssr
2018-06-17	42	1009	3.5	ssr
2019-09-30	45	2234	2.4	ipsos
2019-09-30	47	1115	3.6	Quinnipiac
	library(Synth)
	data(synth.data)

	synth.data$dummy <- rnorm(168, 0, .00001)

	# create matrices from panel data that provide inputs for synth()
	dataprep.out<-
	dataprep(
	foo = synth.data,
	predictors = c("dummy"),

	library(dplyr)
	library(glmnet)
	library(furrr)
	plan("multisession")


	run_simulation <- function(n = 100, num_pred = 50,
	rho = 0, true_beta= -.1,
	error = 0.01){