204Hw3_script.Rmd

---
title: "204Hw3"
author: "Casey Moorhead"
date: "5/15/2018"
output: html_document
---

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

```{r data}

library(tidyverse)

# HW3Data <- read_csv("~/Desktop/ESM 204 - Econ/HW3Data.csv")
setwd("C:/github/esm204_HW3")
HW3Data <- read_csv("HW3Data.csv")

## Change character columns to factors columns and relevel
HW3Data$income<-factor(HW3Data$income, levels=c("poor","middle","rich","very_rich","one_percent")) 
HW3Data$age<-factor(HW3Data$age,levels=c("tothirty","toforty","tofifty","tosixty","oversixty")) 
levels(HW3Data$age)  
class(HW3Data$age)

# Model using glm which is what is generally used for probability/binomial
# glm<-glm(vote~age+income+NEP+bid+risk, data=HW3Data, family="binomial")
# glm
# summary(glm)
```

```{r lm}
# Model using linear model b
datalm<-lm(vote~age+income+NEP+bid+risk, data=HW3Data, family="binomial")
summary(datalm)
# we must assume that anything above 0.5 is a yes vote
 
plot(datalm)

ggplot(data = datalm, aes(x=vote, y=bid))+
  geom_line()

```

2. 
```{r WTP_60%}

# reshuffle the equation to isolate bid (or price).
# Set risk at 60% 
# determine the willingness to pay. 
# calculate the vertical distance between the vertical distance between the original lm and the new
# lm with a risk value of 60%.

```

```{r pick3}
#value of a single prevented whale death

respondent1<-sample_n(HW3Data,1)
respondent2<-sample_n(HW3Data,1)
respondent3<-sample_n(HW3Data,1)

#plug in probability of yes based on calculation, and then calculate the WTP? Change risk to 60.
```