barplot(twoway, legend=T, beside=T)
# Calculate P(A): the probability of each genre being played
prop.table(genre)
# Calculate P(A|B): the probability of each genre being played, given the artist’s gender
prop.table(twoway,1)
table(older$Gender)
older$Gender == "M" & older$Genre == "Jazz/Blues"
sum(older$Gender == "M" & older$Genre == "Jazz/Blues")
prop.table(table(older$gender, older$Genre))
load("~/Desktop/OneDrive/2016 S1/ECMT1010/Week 2/AllCountries.rda")
View(AllCountries)
AllCountries$Country
View(AllCountries)
rem(AllCountries)
rm(AllCountries)
USStates <- read.table("~/Desktop/OneDrive/2016 S1/ECMT1010/Week 2/USStates.rda", header=TRUE, quote="\"")
View(USStates)
View(USStates)
View(USStates)
rm(USStates)
load("~/Desktop/OneDrive/2016 S1/ECMT1010/Week 2/USStates.rda")
View(USStates)
USStates$College
college <- cbind(USStates$State, USStates$College)
View(college)
names(college) <- c("State", "College")
View(college)
names(college) <- c(State, College)
colnames()?
colnames?
colnames()??
colnames(college) <- c("State", "Degree")
View(college)
college <- cbind(USState$State, USStates$College)
college <- cbind(USStates$State, USStates$College)
View(college)
str(states)
states <- USStates
load("~/Desktop/OneDrive/2016 S1/ECMT1010/Week 2/USStates.rda")
states <- USStates
plot(states$IQ, states$College)
abline(lm(states$IQ~states$College))
lm(states$IQ~states$College)
abline(states$IQ~states$College)
abline(lm(states$College~states$IQ))
cor(states$College, states$IQ)
response <- c("great", "fair", "not very", "none")
response <- c("great" = 81, "fair" = 325, "not very" = 397, "none" = 214)
plot(response)
barplot(response)
barplot(response, title = "US Confidence in Mass Media", ylim = 400)
barplot(response, title = "US Confidence in Mass Media")
barplot(response, title = "US Confidence in Mass Media", xpd = FALSE)
warnings()
barplot(response, main = "US Confidence in Mass Media", xpd = FALSE)
barplot(response, main = "US Confidence in Mass Media", xpd = TRUE)
barplot(response, main = "US Confidence in Mass Media", ylim = 400)
barplot(response, main = "US Confidence in Mass Media", ylim = 100)
barplot(response, main = "US Confidence in Mass Media")
response.relfreq <- response/sum(response)
table(response.relfreq)
barplot(response.relfreq)
sum(response.relfreq)
pn <- c("Positive", "Negative")
democrats <- c(0.6, 0.4)
republicans <- c(0.27, 0.73)
independent <- c(0.41, 0.58)
rbind(democrats, independent, republican)
rbind(democrats, independent, republicans)
party < -rbind(democrats, independent, republicans)
party <- rbind(democrats, independent, republicans)
colnames(party) <- c("Positive", "Negative")
party
barplot(party)
barplot(party, besides = TRUE)
barplot(table(party), besides = TRUE)
party <- table(democrats, independent, republicans)
party <- table(democrats, independent, republicans)
colnames(party) <- c("Positive", "Negative")
party
party <- rbind(democrats, independent, republicans)
table(party, pn)
table(party)
table(pn, party)
parties <- c("Democrats", "Independent", "Republican")
positive <- c(0.6, 0.41, 0.27)
negative <- c(0.4, 0.59, 0.73)
pmatrix <- cbind(parties, positive, negative)
View(pmatrix)
table(pmatrix$positive, pmatrix$negative)
table(pmatrix)
30.0-17.8
26.825-22.175
(600-497)/114
(600-514)/117
(600-489)/113
122.5-7.5
1.5*115
122.5+172.5
1.5*42.5
42.5+63.75
(440-90.5)/94.6
(185-30)/49.4
A <- c(48,50,48,44,41)
B <- c(47,46,39,44,42)
cor(A,B)
plot(A,B)
A <- union(A, c(52))
B <- union(B, c(60))
A <- c(48,50,48,44,41)
A <- union(A, c(52))
cor(A,B)
A <- c(48,50,48,44,41)
B <- c(47,46,39,44,42)
A <- union(A, c(52))
A <- c(48,50,48,44,41)
A[6] <- 52
B <- union(B, c(60))
cor(A,B)
bicep <- BodyFat$Bicep
dice <- c(1, 2, 3, 4, 5, 6)
diceout <- boot(dice, 1, 1)
diceout <- boot(dice, 1, 1)
diceout <- boot(dice, 1, 1)
diceout <- boot(dice, 1, 1)
boot(dice, 1, 1)''
boot(dice, 1, 1)
diceout <- boot(dice, k=1, 1)
dice <- c(1, 2, 3, 4, 5, 6)
diceout <- boot(dice, k=1, 1)
dice <- c(1, 2, 3, 4, 5, 6)
dice <- [1:6]
dice <- c[1:6]
dice <- c(1, 2, 3, 4, 5, 6)
sample <- sample(dice, size = 25, replace = TRUE)
sample
sample <- boot(dice, size = 25, replace = TRUE)
sample <- boot(dice, sample, replace = TRUE)
library("boot", lib.loc="/Library/Frameworks/R.framework/Versions/3.2/Resources/library")
sample <- boot(dice, 25)
sample <- boot(dice, k=1, 25)
sample <- boot(dice, k=1, R=25)
sample <- boot(dice, statistic = 1, R=25)
k <- function(dice)
sample <- boot(dice, k, R=25)
sample
sample
sample
mean(sample)
sample <- boot(dice, k, R=1000)
sample <- boot(dice, k, R=1000)
sample <- boot(dice, dice, R=25)
sample <- boot(dice, k, R=25)
rm(sample)
sample <- boot(dice, k, R=25)
sample
dice <- c(1, 2, 3, 4, 5, 6)
outcome <- function(dice)
dicesample <- boot(dice, outcome, R=25)
dicesample
dicesample <- boot(dice, outcome, R=25)
outcome
library("class", lib.loc="/Library/Frameworks/R.framework/Versions/3.2/Resources/library")
outcome
dicesample
dicesample
outcome <- function(dice)
dicesample <- boot(dice, outcome, R=25)
dicesample
outcome <- function(dice) dice
dicesample <- boot(dice, outcome, R=25)
outcome <- function(dice) dice
dicesample <- boot(dice, outcome, R=25)
outcome <- function(dice) dice
dicesample <- boot(dice, outcome, R=25)
outcome <- function(x) x
dicesample <- boot(dice, outcome, R=25)
outcome(1:6)
dicesample <- boot(dice, outcome, R=25)
dicesample <- boot(dice, outcome, R=25)
outcome(1)
outcome <- function(x) x^2
outcome(2)
dicesample <- boot(dice, outcome, R=25)
outcome <- function(dice) x
dicesample <- boot(dice, outcome, R=25)
outcome <- function(dice) x%*%x
dicesample <- boot(dice, outcome, R=25)
dicesample <- boot(dice, outcome(data), R=25)
dicesample <- boot(dice, outcome(dice), R=25)
dicesample <- boot(dice, outcome, R=25)
dicesample <- boot(dice, outcome(x), R=25)
dicesample <- boot(dice, outcome(x), R=25)
dicesample
outcome <- function(x) x%8%x
dicesample <- boot(dice, outcome(x), R=25)
outcome <- function(x) x%*%x
dicesample <- boot(dice, outcome(x), R=25)
outcome <- function(x, dice)
dicesample <- boot(dice, outcome, R=25)
dicesample
outcome <- function(x, dice) dice
dicesample <- boot(dice, outcome, R=25)
dicesample
dicesample <- boot(dice, outcome, R=100)
dicesample
rm(dice)
rm(dicesample)
rm(k)
rm(outcome)
dicesample <- sample(6, 25, replace = TRUE)
dicesample
dicesample <- sample(6, 25, replace = TRUE)
diceboot <- boot(dicesample, R=100)
diceboot <- boot(dicesample, R=100)
rm(dicesample)
dice(1, 1, 6)
install.packages("~/Downloads/dice/R/dice.R", repos = NULL)
install.packages("~/Downloads/dice_1.2.tar.gz", repos = NULL, type = "source")
dice <- c(1, 2, 3, 4, 5, 6)
diceoutcome <- function(dice, x) dice[x%6]
diceoutcome <- function(dice, x) dice(x%6)
sample(dice)
sample(dice, 1)
sample(dice, 25, replace = TRUE)
diceoutcome <- function(dice, x) x%7
diceoutcome <- function(x) x%7
sample <- (dice, 25, replace = TRUE)
outcome <- function(sample) sample
diceboot <- boot(sample, outcome, sim = "parametric")
diceboot <- boot(sample, outcome, R = 25, sim = "parametric")
diceboot <- boot(sample, outcome, R = 24, sim = "parametric")
diceboot <- boot(sample, outcome, R = 24, sim = "balanced")
outcome <- function(sample, x) sample
diceboot <- boot(sample, outcome, R = 24, sim = "balanced")
mean(123, 120, 45, 30, 40, 86, 36, 52, 86, 2, 70, 155, 70, 168, 156, 107, 126, 66, 71, 97, 73, 90, 69, 5, 68)
mean(120, 45, 30, 40, 86, 36, 52, 86, 2, 70, 155, 70, 168, 156, 107, 126, 66, 71, 97, 73, 90, 69, 5, 68)
mean(45, 30, 40, 86, 36, 52, 86, 2, 70, 155, 70, 168, 156, 107, 126, 66, 71, 97, 73, 90, 69, 5, 68)
x <- c(123, 120, 45, 30, 40, 86, 36, 52, 86, 2, 70, 155, 70, 168, 156, 107, 126, 66, 71, 97, 73, 90, 69, 5, 68)
mean(x)
sd()
sd(4.7,5.1,7.9,7,7.1,7.2,7.1,7.2,4.8,5.2,4.6,6.7)
s -> c(4.7,5.1,7.9,7,7.1,7.2,7.1,7.2,4.8,5.2,4.6,6.7)
s <- c(4.7,5.1,7.9,7,7.1,7.2,7.1,7.2,4.8,5.2,4.6,6.7)
sd(s)
o <- c(3.8,4.9,7.4,7.1,6.2,6.1,6.7,7,4.3,5,4.1,6.1)
sd(o)
mean(s)
mean(o)
Students <- read.csv("~/Downloads/Students.csv")
View(Students)
gpa <- Students$GPA
plot(gpa, sat)
sat <- Students$SAT
plot(gpa, sat)
plot(gpa, sat, xlab = "GPA", ylab = "SAT Score")
plot(gpa, sat, xlab = "GPA", ylab = "SAT")
plot(gpa, sat, xlab = "GPA", ylab = "SAT", main = "GPA vs SAT Scores")
cor(gpa, sat)
lm(gpa~sat)
abline(lm(gpa~sat))
abline(lm(sat~gpa))
q4 <- cbind(Students$GPA, Students$Piercings)
View(q4)
q4 <- cbind("GPA" = Students$GPA, "Piercings" = Students$Piercings)
View(q4)
View(q4)
View(q4)
View(Students)
View(q4)
View(q4)
q4 <- cbind("Piercings" = Students$Piercings, "GPA" = Students$GPA)
View(q4)
year <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10
year <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
year <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
hd <- c(50, 45, 54, 49, 54, 66, 59, 68, 54, 62)
plot(year, hd)
abline(lm(year~hd))
abline(lm(hd~year))
cor(hd, year)
coef(lm(hd~year))
year <- c(2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011)
abline(lm(hd~year))
year <- c(2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011)
hd <- c(50, 45, 54, 49, 54, 66, 59, 68, 54, 62)
plot(year, hd)
abline(lm(hd~year))
cor(hd, year)
coef(lm(hd~year))
landarea <- c(88.3, 10.0, 20.1, 425.4, 155.4, 28.5, 229.9, 25.2, 10.2, 120.4, 9147.4)
rural <- c(48.0, 16.3, 16.3, 51.4, 63.2, 33.5, 36.1, 45.8, 33.1, 13, 37.3, 18.3)
abline(lm(rural~landarea))
rural <- c(48.0, 16.3, 51.4, 63.2, 33.5, 36.1, 45.8, 33.1, 13, 37.3, 18.3)
abline(lm(rural~landarea))
plot(abline(lm(rural~landarea)))
abline(lm(rural~landarea)))
plot(landarea, rural)
abline(lm(rural~landarea)))
abline(lm(rural~landarea))
coef(lm(rural~landarea))
landarea <- c(88.3, 10.0, 20.1, 425.4, 155.4, 28.5, 229.9, 25.2, 10.2, 120.4), 9147.4)
rural <- c(48.0, 16.3, 51.4, 63.2, 33.5, 36.1, 45.8, 33.1, 13, 37.3)
landarea <- c(88.3, 10.0, 20.1, 425.4, 155.4, 28.5, 229.9, 25.2, 10.2, 120.4)
plot(landarea, rural)
abline(lm(rural~landarea))
coef(lm(rural~landarea))
Students <- read.csv("~/Downloads/Students.csv")
View(Students)
SAT <- Students$SAT
GPA <- Students$GPA
rm(year)
rm(rural)
rm(landarea)
rm(hd)
plot(SAT, GPA, xlab = "SAT Scores", ylab = "GPA", main = "GPA vs SAT Scores")
cor(SAT, GPA)
lm(GPA~SAT)
SAT <- Students$SAT
Students <- read.csv("~/Downloads/Students.csv")
View(Students)
SAT <- Students$SAT
GPA <- Students$GPA
lm(GPA~SAT)
gsat <- lm(GPA~SAT)
summary(gsat)
help("update)
help("update")
install.packages(c("boot", "cluster", "codetools", "digest", "evaluate", "foreign", "formatR", "highr", "htmltools", "knitr", "lattice", "Matrix", "mgcv", "mime", "nlme", "nnet", "rmarkdown", "stringi", "stringr", "survival", "yaml"))
install.packages("tidyverse")
library(tidyverse)
mpg
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy))
ggplot(data = mpg)
mpg
?mpg
ggplot(data = mpg) +
geom_point(mapping = aes(x = hwy, y = cyl))
ggplot(data = mpg) + geom_point(mapping = aes(x = class, y = drv))
library(tidyverse)
rain <- c(12, 2, 5, 8, 7, 13, 3, 9)
mean(rain)
sd(rain)
sum(rain)
summary(rain)
fivenum(rain)
source('~/Desktop/RBootcamp/scripts/settingupR.R', echo=TRUE)
install.packages(c('plyr','lmtest','ggplot2','gridExtra','stringr',
'knitr','quantreg','xtable','lme4','eeptools','caret'),
repos="http://cran.wustl.edu/")
install.packages(c("plyr", "lmtest", "ggplot2", "gridExtra",
library(lmtest)
update.packages(lmtest)
update.packageStatus()
install.packages("colorspace")
install.packages("colorspace")
install.packages("colorspace")
example(gqtest)
## @knitr , eval=FALSE,echo=TRUE,tidy=FALSE
install_new<-function(mypkg){
if (mypkg %in% installed.packages()) cat("Package already installed")
else{cat("Package not found, so installing with dependencies...\n
Press CTRL C to abort.")
Sys.sleep(5)
install.packages(mypkg,repos="http://cran.wustl.edu/")
}
}
install_new('plyr')
install_new('lmtest')
install_new('ggplot2')
install_new('gridExtra')
install_new('eeptools')
install_new('stringr')
install_new('knitr')
install_new('quantreg')
install_new('xtable')
install_new('lme4')
install_new('caret')
source('~/Desktop/RBootcamp/scripts/settingupR.R', echo=TRUE)
source('~/Desktop/RBootcamp/scripts/settingupR.R', echo=TRUE)
gew
library(lmtest)
example(gqtest)
cls
library(ggplot2)
y <- rt(200, df = 5)
qplot(sample = y, stat = "qq")
options(repos = c(RStudio = "http://rstudio.org/_packages", getOption("repos")))
install.packages("shiny")
install.packages("shiny")
options(repos = c(RStudio = "http://rstudio.org/_packages", getOption("repos")))
install.packages("shiny")
update.packages()
update.packages()
update.packages()
?summary
??regression
ls
cd
source('~/RBootcamp/scripts/Tutorial0.R', echo=TRUE)
data(mtcars)
reset
data(mtcars)
mtcars
summ(mtcars)
summary(mtcars)
hist(rnorm(10000))
ls
ls()
a <- 3
A <- 4
print(c(a,A))
print(a,A)
update()
update.packageStatus()
install.packages(c("backports", "broom", "data.table", "DBI", "digest", "forcats", "ggplot2", "jsonlite", "lmtest", "maptools", "Matrix", "memisc", "mgcv", "nlme", "openssl", "psych", "Rcpp", "rmarkdown", "rprojroot", "selectr", "shiny", "sp", "SparseM", "stringr", "tidyr", "tidyverse", "xml2"))
update()
install.packages(c("assertthat", "backports", "BH", "boot", "car", "caret", "cluster", "curl", "data.table", "DBI", "dplyr", "eeptools", "evaluate", "foreign", "formatR", "gridExtra", "haven", "htmltools", "httpuv", "httr", "jsonlite", "knitr", "lattice", "lme4", "maptools", "markdown", "MASS", "Matrix", "mgcv", "modelr", "openssl", "pbkrtest", "psych", "purrr", "quantreg", "R6", "Rcpp", "RcppEigen", "readr", "readxl", "rmarkdown", "rpart", "scales", "shiny", "sourcetools", "sp", "SparseM", "stringi", "survival", "tibble", "tidyr", "zoo"))
update
install.packages(c("backports", "broom", "car", "caret", "curl", "data.table", "digest", "foreach", "glue", "hms", "iterators", "lazyeval", "lme4", "lubridate", "MASS", "Matrix", "openssl", "quantreg", "Rcpp", "RcppEigen", "recipes", "reshape2", "rlang", "rmarkdown", "robustbase", "rprojroot", "stringi", "tibble", "tidyselect", "tidyverse", "timeDate", "vcd", "withr", "yaml"))
lg(3955)
log(3955)
4.92 + 0.31
1.04 - 1.58
data_vec = seq(from = log(3955)-0.5,to = log(3955)+0.5,by = 0.01)
Y0 = 4.92 + 1.04*data_vec
Y1 = 5.23 -0.54*data_vec
data_vec = seq(from = log(3955)-0.5,to = log(3955)+0.49,by = 0.01)
Y0 = 4.92 + 1.04*data_vec
Y1 = 5.23 -0.54*data_vec
data_vec = seq(from = log(3955)-0.5,to = log(3955)+0.49,by = 0.01)
Y0_0 = 4.92 + 1.04*data_vec[1:50]
Y0_1 = 4.92 + 1.04*data_vec[51:100]
Y1 = 5.23 -0.54*data_vec[51:100]
data_vec = seq(from = log(3955)-0.5,to = log(3955)+0.49,by = 0.01)
Y0_0 = 4.92 + 1.04*data_vec[1:50]
Y0_1 = 4.92 + 1.04*data_vec[51:100]
Y1 = 5.23 -0.54*data_vec[51:100]
plot(data_vec,c(Y0_0,Y1),type = "l", main = "IRF",
xlab = "Quarter",ylab = "",lty = 1)
data_vec = seq(from = log(3955)-0.5,to = log(3955)+0.49,by = 0.01)
Y0_0 = 4.92 + 1.04*data_vec[1:50]
Y0_1 = 4.92 + 1.04*data_vec[51:100]
Y1 = 5.23 -0.54*data_vec[51:100]
plot(data_vec[1:50],Y0_0,type = "l", main = "IRF",
xlab = "Quarter",ylab = "",lty = 1)
lines(data_vec[50:100],y1,lty = 1)
data_vec = seq(from = log(3955)-0.5,to = log(3955)+0.49,by = 0.01)
Y0_0 = 4.92 + 1.04*data_vec[1:50]
Y0_1 = 4.92 + 1.04*data_vec[51:100]
Y1 = 5.23 -0.54*data_vec[51:100]
plot(data_vec[1:50],Y0_0,type = "l", main = "IRF",
xlab = "Quarter",ylab = "",lty = 1)
lines(data_vec[50:100],Y1,lty = 1)
data_vec = seq(from = log(3955)-0.5,to = log(3955)+0.49,by = 0.01)
Y0_0 = 4.92 + 1.04*data_vec[1:50]
Y0_1 = 4.92 + 1.04*data_vec[51:100]
Y1 = 5.23 -0.54*data_vec[51:100]
plot(data_vec[1:50],Y0_0,type = "l", main = "IRF",
xlab = "Quarter",ylab = "",lty = 1)
lines(data_vec[51:100],Y1,lty = 1)
data_vec = seq(from = log(3955)-0.5,to = log(3955)+0.49,by = 0.01)
Y0_0 = 4.92 + 1.04*data_vec[1:50]
Y0_1 = 4.92 + 1.04*data_vec[51:100]
Y1 = 5.23 -0.54*data_vec[51:100]
plot(data_vec[1:50],Y0_0,type = "l", main = "IRF",
xlab = "Quarter",ylab = "",lty = 1)
lines(data_vec[51:100],Y1,lty = 2)
data_vec = seq(from = log(3955)-0.5,to = log(3955)+0.49,by = 0.01)
Y0_0 = 4.92 + 1.04*data_vec
Y0_1 = 4.92 + 1.04*data_vec
Y1 = 5.23 -0.54*data_vec
Y = Y0_0
for(j in 51:100) Y[j] = Y1
plot(data_vec[1:50],Y0_0,type = "l", main = "IRF",
xlab = "Quarter",ylab = "",lty = 1)
data_vec = seq(from = log(3955)-0.5,to = log(3955)+0.49,by = 0.01)
Y0_0 = 4.92 + 1.04*data_vec
Y0_1 = 4.92 + 1.04*data_vec
Y1 = 5.23 -0.54*data_vec
Y = Y0_0
for(j in 51:100) Y[j] = Y1
plot(data_vec,Y,type = "l", main = "IRF",
xlab = "Quarter",ylab = "",lty = 1)
data_vec = seq(from = -0.5,to = 0.49,by = 0.01)
Y0_0 = 4.92 + 1.04*data_vec
Y0_1 = 4.92 + 1.04*data_vec
Y1 = 5.23 -0.54*data_vec
Y = Y0_0
for(j in 51:100) Y[j] = Y1
plot(data_vec,Y,type = "l", main = "IRF",
xlab = "Quarter",ylab = "",lty = 1)
source('~/Desktop/RA/JMCB/JMCB R/uc_yc.R', echo=TRUE)
source('~/Desktop/RA/JMCB/JMCB R/uc_yc.R', echo=TRUE)
source('~/Desktop/RA/JMCB/JMCB R/uc_yc.R', echo=TRUE)
getwd()
source('~/Desktop/RA/JMCB/JMCB R/uc_yc.R', echo=TRUE)
source('~/Desktop/RA/JMCB/JMCB R/uc_yc.R', echo=TRUE)
source('~/Desktop/RA/JMCB/JMCB R/uc_yc.R', echo=TRUE)
source('~/Desktop/RA/JMCB/JMCB R/uc_yc.R', echo=TRUE)
update
install.packages(c("arm", "BH", "bindr", "bindrcpp", "broom", "callr", "car", "caret", "caTools", "cli", "clipr", "cluster", "coda", "curl", "CVST", "data.table", "DBI", "dbplyr", "ddalpha", "digest", "dplyr", "DRR", "eeptools", "evaluate", "forcats", "foreign", "ggplot2", "glue", "haven", "highr", "hms", "httpuv", "ipred", "iterators", "kernlab", "knitr", "lava", "lme4", "lmtest", "lubridate", "maptools", "MASS", "Matrix", "mgcv", "mime", "ModelMetrics", "modelr", "munsell", "nloptr", "openssl", "optimx", "pillar", "pkgconfig", "plogr", "prodlim", "psych", "purrr", "quantreg", "R6", "Rcpp", "RcppEigen", "RcppRoll", "readxl", "recipes", "reprex", "rlang", "rmarkdown", "robustbase", "rpart", "rprojroot", "rstudioapi", "scales", "selectr", "sfsmisc", "shiny", "sourcetools", "sp", "stringi", "stringr", "survival", "tibble", "tidyr", "tidyselect", "timeDate", "utf8", "viridisLite", "withr", "xml2", "xtable", "yaml", "zoo"))
source('~/Desktop/CMS BPP QMLE matlab R/R/BPP_QMLE.R', echo=TRUE)
wd
dir
getwd
getwd()
setwd("/Users/Patrick/Desktop/CMS BPP QMLE matlab R/R")
source('~/Desktop/CMS BPP QMLE matlab R/R/BPP_QMLE.R', echo=TRUE)
source('~/Desktop/CMS BPP QMLE matlab R/R/BPP_QMLE.R', echo=TRUE)
source('~/Desktop/CMS BPP QMLE matlab R/R/BPP_QMLE.R', echo=TRUE)
