Grant Brown grantbrown

## COVID_Analysis.Rmd
---
title: "EID Class - Fall 2021"
author: "Grant Brown"
date: "9/28/2021"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## R ABSEIR - R0 and EAR0 Issue.Rmd
---
title: "R ABSEIR - R0 and EA-R0 issue"
output: html_document
---

## Issue

Modified code below

```{r Setup, echo=TRUE}

## class_test.Rout
==14436== Memcheck, a memory error detector
==14436== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al.
==14436== Using Valgrind-3.15.0 and LibVEX; rerun with -h for copyright info
==14436== Command: /usr/lib64/R/bin/R -f class_test.R --restore --save --no-readline
==14436==
==14441== Memcheck, a memory error detector
==14441== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al.
==14441== Using Valgrind-3.15.0 and LibVEX; rerun with -h for copyright info
==14441== Command: /usr/bin/uname -m
==14441==

## class_test.R
library("XBART")


data(iris)

y <- matrix(0, nrow = nrow(iris), ncol = 3)
for (i in 1:nrow(iris)){
  y[i, as.numeric(iris$Species)[i]] <- 1
}

## gist:6e4c61d31c5d5b20e24f825283831327
sims <- replicate(1000,{
  X <- as.data.frame(matrix(rnorm(200*25), ncol = 25, nrow = 200))
  colnames(X)[1] <- "Y"

  has.cor <- sapply(2:ncol(X), function(i){cor.test(X$Y, X[,i])$p.value < 0.2})
  lm.out.1 <- lm(Y~., data = X)
  lm.out.2 <- lm(Y~., data = X[,c(1, which(has.cor))])

  ftest <- function(lmrslt){
    fs <- summary(lmrslt)$fstatistic

## KikwitPredictionExample.R
library(ABSEIR)
# read in the data set
data(Kikwit1995)

lastTpt <- 80

# Create a model to relate observe data to epidemic process
count <- Kikwit1995$Count
count[(lastTpt+1):length(count)] <- NA
data_model = DataModel(count,

## ConfidencePlot.R
# Functions to make SLR Confidence and Prediction Band Plots.
# Grant Brown, June 2013
# 171-162


# Internal function used by ConfidenceBandPlot and PredictionBandPlot
BasicRegressionPlot = function(X,Y, main = "", xlab = "",ylab = "", plt = TRUE, matchScale = FALSE)
{

## ScoreData.csv

          
            AvgScore
            Age
            Caffeine
            NumberOfGamesPlayed

            
              172.231758565115
              41
              8.27310138781406
              5

            
              344.080816385481
              45
              166.788674010494
              4

            
              158.243974439236
              47
              0
              5

            
              161.762852648026
              52
              0
              4

            
              450.046699030962
              50
              341.247032165972
              7

            
              468.416022742364
              63
              376.400720606928
              4

            
              374.036406183761
              38
              228.098363606319
              3

            
              312.256335733073
              59
              144.104941199508
              11

            
              466.517013106131
              55
              339.59090467707
              6

## MachineData.csv

          
            partQuality
            machineNumber
            speedSetting
            shopTemperature
            shopHumidity
            dayOfWeek
            materialHardness
            materialColor
            volumeRemoved

            
              24
              1
              3
              66
              55
              1
              33.3
              red
              66.4977751111701

            
              100
              2
              4
              82
              63
              2
              75
              green
              141.814186119158

            
              40
              2
              4
              65
              69
              5
              33.3
              red
              52.6887914269801

            
              -2
              2
              1
              67
              60
              5
              33.3
              blue
              90.0792218314648

            
              19
              1
              1
              67
              72
              6
              75
              green
              72.4268319792361

            
              141
              1
              8
              72
              54
              4
              33.3
              red
              160.989211437966

            
              60
              2
              5
              68
              76
              6
              33.3
              red
              100.177360139193

            
              308
              2
              9
              68
              44
              6
              75
              green
              121.351584377482

            
              31
              3
              3
              72
              53
              5
              33.3
              red
              74.7642070437819

## Hw6P2.R

# Change my working directory to wherever the data file is stored
setwd("/home/grantbrown/dev/171-162-Materials/Section4/Homework5/")

CancerData = read.csv("Homework5.csv", head = TRUE)

# Fit the reduced model
ReducedModel = lm(AllCancerMortalityCrude ~ HospitalBeds, data = CancerData)

# Fit the full model
	---
	title: "EID Class - Fall 2021"
	author: "Grant Brown"
	date: "9/28/2021"
	output: html_document
	---

	```{r setup, include=FALSE}
	knitr::opts_chunk$set(echo = TRUE)
	```
	---
	title: "R ABSEIR - R0 and EA-R0 issue"
	output: html_document
	---

	## Issue

	Modified code below

	```{r Setup, echo=TRUE}
	==14436== Memcheck, a memory error detector
	==14436== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al.
	==14436== Using Valgrind-3.15.0 and LibVEX; rerun with -h for copyright info
	==14436== Command: /usr/lib64/R/bin/R -f class_test.R --restore --save --no-readline
	==14436==
	==14441== Memcheck, a memory error detector
	==14441== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al.
	==14441== Using Valgrind-3.15.0 and LibVEX; rerun with -h for copyright info
	==14441== Command: /usr/bin/uname -m
	==14441==
	library("XBART")


	data(iris)

	y <- matrix(0, nrow = nrow(iris), ncol = 3)
	for (i in 1:nrow(iris)){
	y[i, as.numeric(iris$Species)[i]] <- 1
	}
	sims <- replicate(1000,{
	X <- as.data.frame(matrix(rnorm(200*25), ncol = 25, nrow = 200))
	colnames(X)[1] <- "Y"

	has.cor <- sapply(2:ncol(X), function(i){cor.test(X$Y, X[,i])$p.value < 0.2})
	lm.out.1 <- lm(Y~., data = X)
	lm.out.2 <- lm(Y~., data = X[,c(1, which(has.cor))])

	ftest <- function(lmrslt){
	fs <- summary(lmrslt)$fstatistic
	library(ABSEIR)
	# read in the data set
	data(Kikwit1995)

	lastTpt <- 80

	# Create a model to relate observe data to epidemic process
	count <- Kikwit1995$Count
	count[(lastTpt+1):length(count)] <- NA
	data_model = DataModel(count,
	# Functions to make SLR Confidence and Prediction Band Plots.
	# Grant Brown, June 2013
	# 171-162



	# Internal function used by ConfidenceBandPlot and PredictionBandPlot
	BasicRegressionPlot = function(X,Y, main = "", xlab = "",ylab = "", plt = TRUE, matchScale = FALSE)
	{
AvgScore	Age	Caffeine	NumberOfGamesPlayed
172.231758565115	41	8.27310138781406	5
344.080816385481	45	166.788674010494	4
158.243974439236	47	0	5
161.762852648026	52	0	4
450.046699030962	50	341.247032165972	7
468.416022742364	63	376.400720606928	4
374.036406183761	38	228.098363606319	3
312.256335733073	59	144.104941199508	11
466.517013106131	55	339.59090467707	6
partQuality	machineNumber	speedSetting	shopTemperature	shopHumidity	dayOfWeek	materialHardness	materialColor	volumeRemoved
24	1	3	66	55	1	33.3	red	66.4977751111701
100	2	4	82	63	2	75	green	141.814186119158
40	2	4	65	69	5	33.3	red	52.6887914269801
-2	2	1	67	60	5	33.3	blue	90.0792218314648
19	1	1	67	72	6	75	green	72.4268319792361
141	1	8	72	54	4	33.3	red	160.989211437966
60	2	5	68	76	6	33.3	red	100.177360139193
308	2	9	68	44	6	75	green	121.351584377482
31	3	3	72	53	5	33.3	red	74.7642070437819

	# Change my working directory to wherever the data file is stored
	setwd("/home/grantbrown/dev/171-162-Materials/Section4/Homework5/")

	CancerData = read.csv("Homework5.csv", head = TRUE)

	# Fit the reduced model
	ReducedModel = lm(AllCancerMortalityCrude ~ HospitalBeds, data = CancerData)

	# Fit the full model