Michael Sachs sachsmc

## example.html
<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <meta http-equiv="X-UA-Compatible" content="ie=edge" />
	<link rel="stylesheet" href="prodint.css">
    <title>Product integral</title>
  </head>
  <body>

## sweden-excess-mort-analysis.R
#data source:
# https://scb.se/om-scb/nyheter-och-pressmeddelanden/overdodligheten-fortsatter-att-sjunka-efter-toppen-i-april/

library(readxl)
library(excessmort)
library(tidyr)

dset <- read_excel("2020-05-22-preliminar_statistik_over_doda_inkl_eng.xlsx", sheet = 2,
                   skip = 6)
dset2 <- dset[-nrow(dset),]

## forest-plots.R

ggplot(test, aes(x = group.long, y = est, ymin = low, ymax = high, alpha = factor(alpha))) +
  geom_hline(yintercept = 1, color = "grey45", linetype = 2) +
  geom_hline(yintercept = 0, color = "black") +
  geom_text(aes(y = yev, label = ev, alpha = factor(alpha)), size = 3) +
  geom_pointrange(shape = 18)  + coord_flip() + facet_grid(~ type) +
  scale_y_log10(limits = c(1e-3, max(test$high)), breaks = c(0.01, 1, 10), labels = c(0.01, 1, 10)) +
  scale_x_discrete(labels = group.labs) +
  theme(legend.position = "bottom") +
  ylab("Adjusted hazard ratio versus matched general population") +

## dpp-poc.R
X <- rnorm(n, 1)
Y <- -.5 + 1.5 * X + rnorm(n, sd = .5)
enns <- sample(ceiling(runif(n, 200, 2000)))

samp.indi.data <- function(i) {

  Z <- rbinom(enns[i], 1, .5)
  mu.S <- 2.5 + X[i] * Z
  lambda.T <- -1 + Y[i] * Z


## delay_code.Rmd
---
title: "Untitled"
author: "Michael C Sachs"
date: "24 augusti 2016"
output: html_document
---

```{r setup, include=FALSE}
library(knitr)

## permute.R
env.copy <- new.env(parent = parent.env(environment(psdesign$likelihood)))
ps.copy <- psdesign

objs <- ls(environment(psdesign$likelihood))
for(j in objs){
  assign(j, get(j, environment(psdesign$likelihood)), envir = env.copy)
}

assign("Y.trt", mixup(env.copy$Y.trt, env.copy$trtmat[, "Z"]), envir = env.copy)
assign("Y.untrt", mixup(env.copy$Y.untrt, env.copy$untrt.expand[, "Z"]), envir = env.copy)

## download-data.R
## Inside shinyServer(function(input, output, session) {})
 output$downloadData <- downloadHandler(
    filename = function() { paste("NIH-percent-", input$yvar, ".xlsx", sep = "") },
    content = function(file) {


        myStyles <- c(rep("textStyle", ifelse(is.null(byvar2()), 2, 3)),
                    ifelse(input$yvar == "investigators", "countStyle",
                           "moneyStyle"), "percStyle")


## plotROC-custom-labels.R
library(plotROC)
D <- rbinom(100, 1, .4)
fkd <- data.frame(D = D, Y = rnorm(100, mean = D, sd = .5))

p1 <- ggplot(fkd, aes(d = D, m = Y)) + geom_roc(n.cuts = 0)
dat <- ggplot_build(p1)$data[[1]]

mycut.i <- c(10, 50, 90) # find the indices of the labels you want to plot in dat above

p1 + geom_point(data = dat[mycut.i, ],

## .block
license: mit

## .block
license: mit
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8" />
	<meta name="viewport" content="width=device-width, initial-scale=1.0" />
	<meta http-equiv="X-UA-Compatible" content="ie=edge" />
	<link rel="stylesheet" href="prodint.css">
	<title>Product integral</title>
	</head>
	<body>
	#data source:
	# https://scb.se/om-scb/nyheter-och-pressmeddelanden/overdodligheten-fortsatter-att-sjunka-efter-toppen-i-april/

	library(readxl)
	library(excessmort)
	library(tidyr)

	dset <- read_excel("2020-05-22-preliminar_statistik_over_doda_inkl_eng.xlsx", sheet = 2,
	skip = 6)
	dset2 <- dset[-nrow(dset),]

	ggplot(test, aes(x = group.long, y = est, ymin = low, ymax = high, alpha = factor(alpha))) +
	geom_hline(yintercept = 1, color = "grey45", linetype = 2) +
	geom_hline(yintercept = 0, color = "black") +
	geom_text(aes(y = yev, label = ev, alpha = factor(alpha)), size = 3) +
	geom_pointrange(shape = 18) + coord_flip() + facet_grid(~ type) +
	scale_y_log10(limits = c(1e-3, max(test$high)), breaks = c(0.01, 1, 10), labels = c(0.01, 1, 10)) +
	scale_x_discrete(labels = group.labs) +
	theme(legend.position = "bottom") +
	ylab("Adjusted hazard ratio versus matched general population") +
	X <- rnorm(n, 1)
	Y <- -.5 + 1.5 * X + rnorm(n, sd = .5)
	enns <- sample(ceiling(runif(n, 200, 2000)))

	samp.indi.data <- function(i) {

	Z <- rbinom(enns[i], 1, .5)
	mu.S <- 2.5 + X[i] * Z
	lambda.T <- -1 + Y[i] * Z
	---
	title: "Untitled"
	author: "Michael C Sachs"
	date: "24 augusti 2016"
	output: html_document
	---

	```{r setup, include=FALSE}
	library(knitr)
	env.copy <- new.env(parent = parent.env(environment(psdesign$likelihood)))
	ps.copy <- psdesign

	objs <- ls(environment(psdesign$likelihood))
	for(j in objs){
	assign(j, get(j, environment(psdesign$likelihood)), envir = env.copy)
	}

	assign("Y.trt", mixup(env.copy$Y.trt, env.copy$trtmat[, "Z"]), envir = env.copy)
	assign("Y.untrt", mixup(env.copy$Y.untrt, env.copy$untrt.expand[, "Z"]), envir = env.copy)
	## Inside shinyServer(function(input, output, session) {})
	output$downloadData <- downloadHandler(
	filename = function() { paste("NIH-percent-", input$yvar, ".xlsx", sep = "") },
	content = function(file) {


	myStyles <- c(rep("textStyle", ifelse(is.null(byvar2()), 2, 3)),
	ifelse(input$yvar == "investigators", "countStyle",
	"moneyStyle"), "percStyle")
	library(plotROC)
	D <- rbinom(100, 1, .4)
	fkd <- data.frame(D = D, Y = rnorm(100, mean = D, sd = .5))

	p1 <- ggplot(fkd, aes(d = D, m = Y)) + geom_roc(n.cuts = 0)
	dat <- ggplot_build(p1)$data[[1]]

	mycut.i <- c(10, 50, 90) # find the indices of the labels you want to plot in dat above

	p1 + geom_point(data = dat[mycut.i, ],