Hi Scott,
Thanks for this.
Got some questions below:
Thanks
Hugh
Date: Mon, 23 May 2011 17:32:52 -0500
From: scttchamberla...@gmail.com
To: h_a_patie...@hotmail.com
CC: r-help@r-project.org
Subject: Re: [R] Reading Data from mle into excel?
I would read the datasets into a list first, something like this which will
make a list of dataframes:
filenames <- dir() # where only filenames you want to read in are in this
directory
dataframelist <- lapply(filenames, read.csv, header = TRUE, sep = ",")
OK I tried the code you suggest and I get:
filenames<-dir("C:/Documents and Settings/Hugh/My Documents/PhD/Swaption
vols.csv")
> dataframelist<-lapply(filenames, read.csv, header = TRUE, sep = ",")
>
> dataframelist
list()
> list
function (...) .Primitive("list")
Is this correct?
I only actually need one file, all the time series are stored in seperate
columns.
vols=read.csv(file="C:/Documents and Settings/Hugh/My Documents/PhD/Swaption
vols.csv"
, header=TRUE, sep=",")
X<-ts(vols[,2])
Can I still use this format?
You should be able to put the whole procedure, after reading in dataframes,
into one lapply perhaps, e.g.,
lapply(dataframelist, yourfunction)
as for my function the function mle involves calls to other functions?
dcOU<-function(x,t,x0,theta,log=FALSE){
Ex<-theta[1]/theta[2]+(x0-theta[1]/theta[2])*exp(-theta[2]*t)
Vx<-theta[3]^2*(1-exp(-2*theta[2]*t))/(2*theta[2])
dnorm(x,mean=Ex,sd=sqrt(Vx),log=log)
}
OU.lik<-function(theta1,theta2,theta3){
n<-length(X)
dt<-deltat(X)
-sum(dcOU(X[2:n],dt,X[1:(n-1)],c(theta1,theta2,theta3),log=TRUE))
}
require(stats4)
require(sde)
set.seed(1)
#X<-sde.sim(model="OU",theta=c(3,1,2),N=10000,delta=1)
mle(OU.lik,start=list(theta1=1,theta2=1,theta3=1),
method="L-BFGS-B",lower=c(-Inf,-Inf,-Inf),upper=c(Inf,Inf,Inf))->fit
summary(fit)
Should I store each function is a seperate script?
If so how do I then make sure that they are available in the "workspace"?
Assuming that vols contains the dataframelist who would I call the mle function
using lapply like you showed?
where dataframelist is a list of dataframes, and yourfunction is a function
that does all the procedures for one dataset. The function 'yourfunction' will
be applied to each dataset in the list separately, then the results output into
a list.
Then, if the results from each dataset will have the same dimensions, you can
do something like ldply using package plyr
ldply(output, 'identity') # where 'output' is the output list of results from
the lapply call above
This will give you a data frame of all the results.
Scott Chamberlain
Rice University, EEB Dept.
On Monday, May 23, 2011 at 4:32 PM, Bazman76 wrote:
Hi there,
I ran the following code:
vols=read.csv(file="C:/Documents and Settings/Hugh/My Documents/PhD/Swaption
vols.csv"
, header=TRUE, sep=",")
X<-ts(vols[,2])
#X
dcOU<-function(x,t,x0,theta,log=FALSE){
Ex<-theta[1]/theta[2]+(x0-theta[1]/theta[2])*exp(-theta[2]*t)
Vx<-theta[3]^2*(1-exp(-2*theta[2]*t))/(2*theta[2])
dnorm(x,mean=Ex,sd=sqrt(Vx),log=log)
}
OU.lik<-function(theta1,theta2,theta3){
n<-length(X)
dt<-deltat(X)
-sum(dcOU(X[2:n],dt,X[1:(n-1)],c(theta1,theta2,theta3),log=TRUE))
}
require(stats4)
require(sde)
set.seed(1)
#X<-sde.sim(model="OU",theta=c(3,1,2),N=10000,delta=1)
mle(OU.lik,start=list(theta1=1,theta2=1,theta3=1),
method="L-BFGS-B",lower=c(-Inf,-Inf,-Inf),upper=c(Inf,Inf,Inf))->fit
summary(fit)
#ex3.01 R
prof<-profile(fit)
par(mfrow=c(1,3))
plot(prof)
par(mfrow=c(1,1))
vcov(fit)
I run the code above and I get:
summary(fit)
Maximum likelihood estimation
Call:
mle(minuslogl = OU.lik, start = list(theta1 = 1, theta2 = 1,
theta3 = 1), method = "L-BFGS-B", lower = c(-Inf, -Inf, -Inf),
upper = c(Inf, Inf, Inf))
Coefficients:
Estimate Std. Error
theta1 0.03595581 0.013929892
theta2 4.30910365 1.663781710
theta3 0.02120220 0.004067477
-2 log L: -5136.327
I need to run the same analysis for 40 different time series.
I want to be able to collate all the estimates of theta and the associated
stadard errors and then transfer them into excel?
Can someone please point me to some R code that will allow me to do this?
Thanks
--
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