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rentersAndOwners.Rmd
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rentersAndOwners.Rmd
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---
title: "R Notebook"
output:
pdf_document:
keep_tex: yes
latex_engine: xelatex
html_notebook: default
keep_tex: yes
---
1. P_housing
1a.graph change in house prices
1b.look for an inflection point -
?? any evidence of a slowdown in price appreciation ??
2. try to say something about renters vs. owners
2a.?? any difference ??
2b.?? are they like Tokyo renters or San Francisco renters ??
```{r, cache=TRUE, include=FALSE}
library(censusFunctions)
library(ggplot2)
source("./includes/getHousePrices.R", echo=FALSE)
source("./includes/importPrices.R", echo=FALSE)
source("./includes/getGDP.R", echo=FALSE)
source("./includes/graphGDPhouseholdConsumption.R",echo=FALSE)
source("./includes/groupHouseSize.R",echo=FALSE)
```
Here I have added a vertical line where it seems that housing prices, at least in some areas began to slow their ascent.
It looks like after my line at least Gush Dan and Haifa prices grow at a slower rate.
I now attempt to find this inflection point.
```{r,cache=TRUE}
plot1.5rooms<-ggplot(data=p1.5,aes(x=quarter,
y=value,
group=variable
))+geom_line(aes(colour=variable))+
ggtitle("Average P. 1.5-2.5 bedroom home:2006-2016q3")+theme(legend.title=element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x=element_blank())+ ylab("NIS x 100k")+geom_vline(xintercept=28)
plot1.5rooms
library(inflection)
#ede(p1.5$quarter,p1.$value,1)
#func = splinefun(x=p1.5$quarter,y=p1.5$value, method="fmm",ties=mean)
#zzz <-spline(x=p1.5$quarter,y=p1.5$value, method="fmm",ties=mean)
#qplot(spline)
#dValue <-diff(p1.5$value)
#dValue1<-c(NA,dValue)
#p1.5$dValue <-dValue1
#ggplot(data=p1.5,aes(x=quarter, y=dValue))
#qplot(func)
#lm(data=p1.5, formula=log(quarter) ~ log(value))
```
try the `lm` code alli.mod1 = lm(lnWeight ~ lnLength, data = alligator).
```{r}
alligator = data.frame(
lnLength = c(3.87, 3.61, 4.33, 3.43, 3.81, 3.83, 3.46, 3.76,
3.50, 3.58, 4.19, 3.78, 3.71, 3.73, 3.78),
lnWeight = c(4.87, 3.93, 6.46, 3.33, 4.38, 4.70, 3.50, 4.50,
3.58, 3.64, 5.90, 4.43, 4.38, 4.42, 4.25)
)
plot(lnWeight ~ lnLength, data = alligator,
xlab = "Snout vent length (inches) on log scale",
ylab = "Weight (pounds) on log scale",
main = "Alligators in Central Florida"
)
alli.mod1 = lm(lnWeight ~ lnLength, data = alligator)
summary(alli.mod1)
```
Now plot something from the
linear model of the alligators against the
original data.
```{r}
# plot(resid(alli.mod1) ~ fitted(alli.mod1),
# xlab = "Fitted Values",
# ylab = "Residuals",
# main = "Residual Diagnostic Plot",
# panel = function(x, y, ...)
# {
# panel.grid(h = -1, v = -1)
# panel.abline(h = 0)
# panel.xyplot(x, y, ...)
# }
# )
```
try the R package bcp, work an example from the pdf
```{r}
library(bcp)
##### univariate sequential data #####
# an easy problem with 2 true change points
set.seed(5)
x <- c(rnorm(50), rnorm(50, 5, 1), rnorm(50))
bcp.1a <- bcp(x)
plot(bcp.1a, main="Univariate Change Point Example")
#legacyplot(bcp.1a)
```
I think that only has frequencies of X. Let's see if the 2nd example actually has some Ys.
```{r}
# a hard problem with 1 true change point
set.seed(5)
x <- rep(c(0,1), each=50)
y <- x + rnorm(50, sd=1)
bcp.1b <- bcp(y)
plot(bcp.1b, main="Univariate Change Point Example")
```
I don't know what that was, but it still doesn't look right. I will try the 3rd example from the bcp manual.
https://cran.r-project.org/web/packages/bcp/bcp.pdf
```{r}
##### multivariate sequential data #####
# an easy problem in k=3 dimensions
set.seed(5)
x <- rnorm(6, sd=3)
y <- rbind(cbind(rnorm(50, x[1]), rnorm(50, x[2]), rnorm(50, x[3])),
cbind(rnorm(50, x[4]), rnorm(50, x[5]), rnorm(50, x[6])))
bcp.2a <- bcp(y)
plot(bcp.2a, main="Multivariate (k=3) Change Point Example")
plot(bcp.2a, separated=TRUE, main="Multivariate (k=3) Change Point Example")
```
```