Protonk

#Computes a very basic Theil index
i<-1
theil<-matrix(0,4,3)
for (i in i:4) {
    theil[i,1]<-0.25*(gini[i,1]/0.25)*log(gini[i,1]/0.25)
    theil[i,2]<-0.25*(gini[i,2]/0.25)*log(gini[i,2]/0.25)
    theil[i,3]<-0.25*(gini[i,3]/0.25)*log(gini[i,3]/0.25)
}
theil<-rbind(theil,apply(theil,2,sum))
colnames(theil)<-c("Region A","Region B","Region C");

Protonk

x<-c(1:50)
y<-x+rnorm(50,sd=10)

plot(x,y,pch=20,main="Distance from Fitted Line")
abline(coef(lm(y~x)[1],coef(lm(y~x)[2])),col="blue",lwd=2)
segments(x,predict(lm(y~x)),y1=lm(y~x)$model[,1])

#This bit took longer to do than I care to admit.  Basically you want distance from the data point to the line 
#as an adjacent side to the triangle that would have the vertical distance as the hypotenuse.
#Because I don't know how to tell R to draw a segment with a given slope and starting point, I have to give

Protonk

library(MASS)


sigma.test<-matrix(c(8,3,5,8),2,2)
biv.norm<-mvrnorm(200,mu=rep(0, 2),Sigma=sigma.test)
biv.pca<-prcomp(biv.norm,center=FALSE)

#An array is just a high dimensional matrix.  So this is 2x2x50.  There is nothing special about the number 50, I just picked that to smooth the 
#animation.
#The numbers in the sequence make transition.ar[,,1] the 2x2 identity matrix and transition.ar[,,50] the final transformed rotation matrix from 

Protonk

target<-utf8ToInt("Hello World")

#####Random Mating with Mutation.  No selective breeding at all.

#The generation array stores each generation (duh)
generation<- array(0,c(100,11,100))
generation[,,1]<- t(replicate(100, round(runif(11,0,255))))
#Two for loops is generally an R no-no, but since the computation is explicitly serial, I wasn't sure how to get around it
#However I was able to put most of the random number generation in the outer loop and use indices to pick and choose which 
#ones I wanted.

Protonk

bisect <- function(f, u, v, eps){
     if ( f(u) * f(v) > 0)
         stop (" error, select another value for u or v...")
     if ( f(u) < 0) {
         u1 <- u
         v1 <- v
     } else {
         u1 <- v
         v1 <- u
     }

Protonk

#Very simple fitness function.  Works sensibly only on 1 dimensional numeric vectors, but that is 
#ok for this example
fitness.hw<- function(tar.hw,meas.hw,type=c("abs","sqr")) {
	diff.hw<-tar.hw-meas.hw
	if (type=="abs") {
	rowSums(abs(diff.hw))
	}
	else if (type=="sqr") {
	rowSums(diff.hw^2)
	}

Protonk

####Pre-processing of launch log before sending it to Google Refine####
#The vector of widths can be grabbed a number of ways: by hand, with regular expressions, or from a file explaining the structure
launch.width<-c(12,27,15,31,26,9,24,16,9,12,5,19)
#Your file path will vary, of course
launch.path<-c("/Users/protonk/Dropbox/Past Econ Work/Launch vehicle market/Complete text launch log.txt")
launch.names<- c("Launch","Date","COSPAR","PL.Name","Orig.PL.Name","SATCAT","LV.Type","LV.SN","Site","Subsite","Success","Ref")
#Read in FWF and export a .csv
launch.test<-read.fwf(launch.path,launch.width,header=FALSE,skip=2,as.is=TRUE,col.names=launch.names)
write.csv(launch.test,file="~/R/launch.csv")
##########

Protonk

#ADF tests for both come from the function afd.test() in tseries.  The means are just simple means over the ranges I chose.
####Stationary Process####
set.seed(42)
y<- w<- rnorm(1000)
for (t in 2:1000) {
	y[t]<- 0.9*y[t-1]+w[t]
	}
#Vector for line segments. 
xy.mat<- cbind(c(0,450,225,750,600,1000),c(6.5,6.5,7.5,7.5,8.5,8.5))
##Plot the series, with annotations for means and the Dicky-Fuller Test##

Protonk

####Playing with Truncated Normals####

set.seed(42)
x<-rnorm(200,0,4)
y<-x[x>-2]

plot(density(y),xlim=c(-16,16),main="Truncated Normal Distribution",xlab="x")
lines(density(x),col=2)
segments(x0=-2,y0=0,y1=0.15,lty=2,lwd=2)
legend(6,0.125,legend=c("Original","Truncated","Truncation\nPoint"),col=c(2,1,1),lty=c(1,1,2));

Protonk

#loads the packae which allows us to import state files
library(foreign)
#The file can also be a url to the data on your website, obviously if you load it
#on your own PC the path will be different. :)
adams<-read.dta(file="~/Downloads/assignment1.dta")
#Removes columns which aren't used and moves the eph column
adams<-adams[,c(7,2:6,8,9,14,15)]

#This next section converts the binary factors in the state file to factors as
#R is used to seeing them, one column with a specific level for each (hschool, college)