[R] problems with garchFit
Hi all,
I post it on both r-help and r-finance since I don't know where is most
appropriate for this topic. Sorry if it bothers you.
I did garch fitting on SP500 monthly returns with garchFit from fSeries. I
got same coefficients from all cond.dist except normal. I thought that is
probabaly usual for the data. But when I play with it, I got another
question.
I plot skew normal with skew = 1 and a standard normal, they overlap
eachother, so I think they are the same. Skew = 1 means no skewness (I can
not find the paper defining the distribution).
library(fSeries)
curve(dsnorm(x, 0, 1, 1), -2, 2, add = F, col = 'red') #skew normal with
skew 1
curve(dnorm(x, 0, 1), -2, 2, add = T, col = 'blue') #normal
Then I try them as innovations,
#normal innovation
garch_norm - garchFit(series = logr, include.mean = F)
#skew normal innovation
#this line do not include skew, so it got same result as normal
garch_snorm - garchFit(series = logr, cond.dist = 'dsnorm', include.mean =
F, include.skew = F)
#this line includes skew, but use default skew = 1, and it got results
different from normal, which I don't understand
garch_snorm - garchFit(series = logr, cond.dist = 'dsnorm', include.mean =
F, include.skew = T)
Have I done something wrong? I am attaching the code, thank you.
Tian
#GARCH analysis of monthly return
rm(list=ls(all=TRUE))
sp500 - read.csv('sp_m90.csv', header=TRUE)
sp500 - sp500[,2] #only adjusted close
n - length(sp500)
logr - log(sp500[1:n-1] / sp500[2:n])
acf(logr)
ar5 - arima(logr, order = c(5, 0, 0), include.mean = T)
logr- ar5$res
acf(logr)
#fit GARCH distribution
hist(logr, freq = F, ylim = c(0, 12), breaks = 'FD')
norm_fit - normFit(logr)
curve(dnorm(x, norm_fit$est[1], norm_fit$est[2]), -.15, .15, add = TRUE,
col=2)
t_fit - stdFit(logr)
curve(dstd(x, t_fit$est[1], t_fit$est[2], t_fit$est[3]), -.15, .15, add =
TRUE, col=6)
snorm_fit - snormFit(logr)
curve(dsnorm(x, snorm_fit$est[1], snorm_fit$est[2], snorm_fit$est[3]), -.25,
.15, add = TRUE, col=4)
st_fit - sstdFit(logr)
curve(dsstd(x, st_fit$est[1], st_fit$est[2], st_fit$est[3], st_fit$est[4]),
-.25, .15, add = TRUE, col=3)
library(fSeries)
#normal innovation
garch_norm - garchFit(series = logr, include.mean = F)
#t inovation
garch_t - garchFit(series = logr, cond.dist = 'dstd', include.mean = F,
include.shape = T)
garch_t1 - garchFit(series = logr, cond.dist = 'dstd', include.mean = F,
shape = t_fit$est[3], include.shape = T)
#skew normal innovation
garch_snorm - garchFit(series = logr, cond.dist = 'dsnorm', include.mean =
F, include.skew = T)
garch_snorm1 - garchFit(series = logr, cond.dist = 'dsnorm', include.mean =
F, skew = snorm_fit$est[3], include.skew = T)
#skew t innovation
garch_st - garchFit(series = logr, cond.dist = 'dsstd', include.mean = F,
include.skew = T, include.shape = T)
garch_st1 - garchFit(series = logr, cond.dist = 'dsstd', include.mean = F,
skew = st_fit$est[4], shape = st_fit$est[3], include.skew = T, include.shape= T)
vix - read.csv('D:/Documents and Settings/Mu Tian/Desktop/8780/8780
project/vix_m.csv', header=TRUE)
vix - (vix[,2]/100) / (12^.5)
plot_sd - function(x, ylim = null, col = null, ...) {
xcsd = [EMAIL PROTECTED]
plot(xcsd, type = l, col = col, ylab = x,
main = Conditional SD, ylim = ylim)
abline(h = 0, col = grey, lty = 3)
grid()
}
plot_sd(garch_norm, ylim = c(0.02, 0.13), col = 2)
xcsd = [EMAIL PROTECTED]
lines(xcsd, col = 3)
lines(1:n, vix)
#predict
predict(garch_norm)
predict(garch_t)
#demonstration of skew distributions
#skew normal
curve(dsnorm(x, 0, 1, .1), -2, 2, add = F, col = 'green')
curve(dsnorm(x, 0, 1, snorm_fit$est[3]), type = 'l', col = 'blue', add = T)
curve(dsnorm(x, 0, 1, 1), -2, 2, add = T, col = 'red') #normal
#skew t
curve(dsstd(x, 0, 1, 4, 1), -2, 2, add = F, col = 'red')
curve(dsstd(x, 0, 1, st_fit$est[3], st_fit$est[4]), type = 'l', col =
'blue', add = T)
curve(dsstd(x, 0, 1, 100, .5), -2, 2, add = T, col = 'green')
#t
curve(dstd(x, 0, 1, 4), -2, 2, add = T, col = 'red')
curve(dstd(x, 0, 1, t_fit$est[3]), type = 'l', col = 'blue', add = T)
curve(dstd(x, 0, 1, 100), -2, 2, add = T, col = 'green')
curve(dsnorm(x, 0, 1, 1), -2, 2, add = F, col = 'red') #normal
curve(dnorm(x, 0, 1), -2, 2, add = T, col = 'blue') #normal
curve(dsnorm(x, 0, 1, .1), -2, 2, add = T, col = 'green') #normal
[[alternative HTML version deleted]]
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] problems with garchFit
Hi all,
Thank you for responses. If any one need the data, I can email it to you. I
don't think I can attach it to R-help. It is only SP 500 monthly returns I
downloaded from Yahoo finance, with only date and adj. close kept.
Thank you,
Tian
On 11/22/06, T Mu [EMAIL PROTECTED] wrote:
Hi all,
I post it on both r-help and r-finance since I don't know where is most
appropriate for this topic. Sorry if it bothers you.
I did garch fitting on SP500 monthly returns with garchFit from fSeries.
I got same coefficients from all cond.dist except normal. I thought that
is probabaly usual for the data. But when I play with it, I got another
question.
I plot skew normal with skew = 1 and a standard normal, they overlap
eachother, so I think they are the same. Skew = 1 means no skewness (I can
not find the paper defining the distribution).
library(fSeries)
curve(dsnorm(x, 0, 1, 1), -2, 2, add = F, col = 'red') #skew normal with
skew 1
curve(dnorm(x, 0, 1), -2, 2, add = T, col = 'blue') #normal
Then I try them as innovations,
#normal innovation
garch_norm - garchFit(series = logr, include.mean = F)
#skew normal innovation
#this line do not include skew, so it got same result as normal
garch_snorm - garchFit(series = logr, cond.dist = 'dsnorm', include.mean= F,
include.skew = F)
#this line includes skew, but use default skew = 1, and it got results
different from normal, which I don't understand
garch_snorm - garchFit(series = logr, cond.dist = 'dsnorm', include.mean= F,
include.skew = T)
Have I done something wrong? I am attaching the code, thank you.
Tian
#GARCH analysis of monthly return
rm(list=ls(all=TRUE))
sp500 - read.csv('sp_m90.csv', header=TRUE)
sp500 - sp500[,2] #only adjusted close
n - length(sp500)
logr - log(sp500[1:n-1] / sp500[2:n])
acf(logr)
ar5 - arima(logr, order = c(5, 0, 0), include.mean = T)
logr- ar5$res
acf(logr)
#fit GARCH distribution
hist(logr, freq = F, ylim = c(0, 12), breaks = 'FD')
norm_fit - normFit(logr)
curve(dnorm(x, norm_fit$est[1], norm_fit$est[2]), -.15, .15, add = TRUE,
col=2)
t_fit - stdFit(logr)
curve(dstd(x, t_fit$est[1], t_fit$est[2], t_fit$est[3]), -.15, .15, add =
TRUE, col=6)
snorm_fit - snormFit(logr)
curve(dsnorm(x, snorm_fit$est[1], snorm_fit$est[2], snorm_fit$est[3]),
-.25, .15, add = TRUE, col=4)
st_fit - sstdFit(logr)
curve(dsstd(x, st_fit$est[1], st_fit$est[2], st_fit$est[3],
st_fit$est[4]), -.25, .15, add = TRUE, col=3)
library(fSeries)
#normal innovation
garch_norm - garchFit(series = logr, include.mean = F)
#t inovation
garch_t - garchFit(series = logr, cond.dist = 'dstd', include.mean = F,
include.shape = T)
garch_t1 - garchFit(series = logr, cond.dist = 'dstd', include.mean = F,
shape = t_fit$est[3], include.shape = T)
#skew normal innovation
garch_snorm - garchFit(series = logr, cond.dist = 'dsnorm', include.mean= F,
include.skew = T)
garch_snorm1 - garchFit(series = logr, cond.dist = 'dsnorm', include.mean=
F, skew = snorm_fit$est[3],
include.skew = T)
#skew t innovation
garch_st - garchFit(series = logr, cond.dist = 'dsstd', include.mean = F,
include.skew = T, include.shape = T)
garch_st1 - garchFit(series = logr, cond.dist = 'dsstd', include.mean =
F, skew = st_fit$est[4], shape = st_fit$est[3], include.skew = T,
include.shape = T)
vix - read.csv('D:/Documents and Settings/Mu Tian/Desktop/8780/8780
project/vix_m.csv', header=TRUE)
vix - (vix[,2]/100) / (12^.5)
plot_sd - function(x, ylim = null, col = null, ...) {
xcsd = [EMAIL PROTECTED]
plot(xcsd, type = l, col = col, ylab = x,
main = Conditional SD, ylim = ylim)
abline(h = 0, col = grey, lty = 3)
grid()
}
plot_sd(garch_norm, ylim = c(0.02, 0.13), col = 2)
xcsd = [EMAIL PROTECTED]
lines(xcsd, col = 3)
lines(1:n, vix)
#predict
predict(garch_norm)
predict(garch_t)
#demonstration of skew distributions
#skew normal
curve(dsnorm(x, 0, 1, .1), -2, 2, add = F, col = 'green')
curve(dsnorm(x, 0, 1, snorm_fit$est[3]), type = 'l', col = 'blue', add =
T)
curve(dsnorm(x, 0, 1, 1), -2, 2, add = T, col = 'red') #normal
#skew t
curve(dsstd(x, 0, 1, 4, 1), -2, 2, add = F, col = 'red')
curve(dsstd(x, 0, 1, st_fit$est[3], st_fit$est[4]), type = 'l', col =
'blue', add = T)
curve(dsstd(x, 0, 1, 100, .5), -2, 2, add = T, col = 'green')
#t
curve(dstd(x, 0, 1, 4), -2, 2, add = T, col = 'red')
curve(dstd(x, 0, 1, t_fit$est[3]), type = 'l', col = 'blue', add = T)
curve(dstd(x, 0, 1, 100), -2, 2, add = T, col = 'green')
curve(dsnorm(x, 0, 1, 1), -2, 2, add = F, col = 'red') #normal
curve(dnorm(x, 0, 1), -2, 2, add = T, col = 'blue') #normal
curve(dsnorm(x, 0, 1, .1), -2, 2, add = T, col = 'green') #normal
[[alternative HTML version deleted]]
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting
Re: [R] problems with garchFit
], st_fit$est[3], st_fit$est[4]),
-.25, .15, add = TRUE, col=3)
legend('topleft', '---Normal', text.col = 'red', bty = 'n')
legend('topleft', '---Student t', text.col = 6, bty = 'n', inset = c(0,
.05))
legend('topleft', '---Skew Normal', text.col = 'blue', bty = 'n', inset =
c(0, .1))
legend('topleft', '---Skew t', text.col = 'green', bty = 'n', inset = c(0,
.15))
library(fSeries)
#normal innovation
garch_norm - garchFit(series = logr, include.mean = F)
#skew normal innovation
garch_snorm1 - garchFit(series = logr, cond.dist = 'dsnorm', include.mean =
F, skew = snorm_fit$est[3], include.skew = T)
#t inovation
garch_t - garchFit(series = logr, cond.dist = 'dstd', include.mean = F,
include.shape = T)
garch_t1 - garchFit(series = logr, cond.dist = 'dstd', include.mean = F,
shape = t_fit$est[3], include.shape = T)
#skew t innovation
garch_st - garchFit(series = logr, cond.dist = 'dsstd', include.mean = F,
include.skew = T, include.shape = T)
garch_st1 - garchFit(series = logr, cond.dist = 'dsstd', include.mean = F,
skew = st_fit$est[4], shape = st_fit$est[3], include.skew = T, include.shape= T)
garch_st2 - garchFit(series = logr, cond.dist = 'dsstd', include.mean = F,
skew = st_fit$est[4], shape = st_fit$est[3], include.skew = F, include.shape= F)
garch_st3 - garchFit(series = logr, cond.dist = 'dsstd', include.mean = F,
skew = st_fit$est[4], shape = st_fit$est[3])
garch_st4 - garchFit(series = logr, cond.dist = 'dsstd', include.mean = F)
plot_sd - function(x, ylim = null, col = null, ...) {
xcsd = [EMAIL PROTECTED]
plot(xcsd, type = l, col = col, ylab = x,
main = Conditional SD, ylim = ylim)
abline(h = 0, col = grey, lty = 3)
grid()
}
plot_sd(garch_norm, ylim = c(0.02, 0.13), col = 2)
xcsd = [EMAIL PROTECTED]
lines(xcsd, col = 3)
legend('topleft', '---Normal innovation', text.col = 'red', bty = 'n', inset
= c(0, .05))
legend('topleft', '---Skew innovation', text.col = 'green', bty = 'n', inset
= c(0, .1))
On 11/22/06, Gabor Grothendieck [EMAIL PROTECTED] wrote:
As a courtesy to others you might summarize the responses to the list.
Also,
I suggest you also include the data by issuing the command:
dput(x, control = all) # x is your data
and pasting its result into your post so others can reproduce
what you have done.
On 11/22/06, T Mu [EMAIL PROTECTED] wrote:
Hi all,
Thank you for responses. If any one need the data, I can email it to
you. I
don't think I can attach it to R-help. It is only SP 500 monthly
returns I
downloaded from Yahoo finance, with only date and adj. close kept.
Thank you,
Tian
On 11/22/06, T Mu [EMAIL PROTECTED] wrote:
Hi all,
I post it on both r-help and r-finance since I don't know where is
most
appropriate for this topic. Sorry if it bothers you.
I did garch fitting on SP500 monthly returns with garchFit from
fSeries.
I got same coefficients from all cond.dist except normal. I thought
that
is probabaly usual for the data. But when I play with it, I got
another
question.
I plot skew normal with skew = 1 and a standard normal, they overlap
eachother, so I think they are the same. Skew = 1 means no skewness (I
can
not find the paper defining the distribution).
library(fSeries)
curve(dsnorm(x, 0, 1, 1), -2, 2, add = F, col = 'red') #skew normal
with
skew 1
curve(dnorm(x, 0, 1), -2, 2, add = T, col = 'blue') #normal
Then I try them as innovations,
#normal innovation
garch_norm - garchFit(series = logr, include.mean = F)
#skew normal innovation
#this line do not include skew, so it got same result as normal
garch_snorm - garchFit(series = logr, cond.dist = 'dsnorm',
include.mean= F,
include.skew = F)
#this line includes skew, but use default skew = 1, and it got results
different from normal, which I don't understand
garch_snorm - garchFit(series = logr, cond.dist = 'dsnorm',
include.mean= F,
include.skew = T)
Have I done something wrong? I am attaching the code, thank you.
Tian
#GARCH analysis of monthly return
rm(list=ls(all=TRUE))
sp500 - read.csv('sp_m90.csv', header=TRUE)
sp500 - sp500[,2] #only adjusted close
n - length(sp500)
logr - log(sp500[1:n-1] / sp500[2:n])
acf(logr)
ar5 - arima(logr, order = c(5, 0, 0), include.mean = T)
logr- ar5$res
acf(logr)
#fit GARCH distribution
hist(logr, freq = F, ylim = c(0, 12), breaks = 'FD')
norm_fit - normFit(logr)
curve(dnorm(x, norm_fit$est[1], norm_fit$est[2]), -.15, .15, add =
TRUE,
col=2)
t_fit - stdFit(logr)
curve(dstd(x, t_fit$est[1], t_fit$est[2], t_fit$est[3]), -.15, .15,
add =
TRUE, col=6)
snorm_fit - snormFit(logr)
curve(dsnorm(x, snorm_fit$est[1], snorm_fit$est[2], snorm_fit$est[3]),
-.25, .15, add = TRUE, col=4)
st_fit - sstdFit(logr)
curve(dsstd(x, st_fit$est[1], st_fit$est[2], st_fit$est[3],
st_fit$est[4]), -.25, .15, add = TRUE
[R] questions about garchFit
Hi all,
I was trying garchFIt() of fSeries to fit volatility of monthly log returns
of SP500. I tried residuals of normal, student t, skew normal, skew t. But
all innovations except normal got exaxtly same coefficients, even if I
changed their parameters of skew and shape.
Is this correct for the data or something wrong? I am attaching the code,
thank you.
Muster
#GARCH analysis of monthly return
rm(list=ls(all=TRUE))
sp500 - read.csv('sp_m.csv', header=TRUE)
sp500 - sp500[,2] #only adjusted close
n - length(sp500)
logr - log(sp500[1:n-1] / sp500[2:n])
acf(logr)
ar5 - arima(logr, order = c(5, 0, 0), include.mean = T)
logr- ar5$res #remove mean
acf(logr)
#fit GARCH distribution
hist(logr, freq = F, ylim = c(0, 12), breaks = 'FD')
norm_fit - normFit(logr)
curve(dnorm(x, norm_fit$est[1], norm_fit$est[2]), -.15, .15, add = TRUE,
col=2)
t_fit - stdFit(logr)
curve(dstd(x, t_fit$est[1], t_fit$est[2], t_fit$est[3]), -.15, .15, add =
TRUE, col=6)
snorm_fit - snormFit(logr)
curve(dsnorm(x, snorm_fit$est[1], snorm_fit$est[2], snorm_fit$est[3]), -.25,
.15, add = TRUE, col=4)
st_fit - sstdFit(logr)
curve(dsstd(x, st_fit$est[1], st_fit$est[2], st_fit$est[3], st_fit$est[4]),
-.25, .15, add = TRUE, col=3)
library(fSeries)
#normal innovation
garch_norm - garchFit(series = logr, include.mean = F)
#t inovation
garch_t - garchFit(series = logr, cond.dist = 'dstd', include.mean = F,
include.shape = T)
garch_t1 - garchFit(series = logr, cond.dist = 'dstd', include.mean = F,
shape = t_fit$est[3], include.shape = T)
#skew normal innovation
garch_snorm - garchFit(series = logr, cond.dist = 'dsnorm', include.mean =
F, include.skew = T)
garch_snorm1 - garchFit(series = logr, cond.dist = 'dsnorm', include.mean =
F, skew = snorm_fit$est[3], include.skew = T)
#skew t innovation
garch_st - garchFit(series = logr, cond.dist = 'dsstd', include.mean = F,
include.skew = T, include.shape = T)
garch_st1 - garchFit(series = logr, cond.dist = 'dsstd', include.mean = F,
skew = st_fit$est[4], shape = st_fit$est[3], include.skew = T, include.shape= T)
[[alternative HTML version deleted]]
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] R gui console problem
Hi all, My R GUI got a weird perk. It loads only first page of scripts, about 28 rows. I didn't change any configuration or installed anything special. I use R 2.3.1, windows. Help please. Thank you. [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] confusing about contrasts concept
Hi all, Where can I find a thorough explanation of Contrasts and Contrasts Matrices? I read some help but still confused. Thank you, Tian [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] confusing about contrasts concept
Thank you. Got both. On 8/16/06, Peter Alspach [EMAIL PROTECTED] wrote: Tian Bill Venables wrote an excellent explanation to the S list back in 1997. I saved it as a pdf file and attach it herewith ... Peter Alspach -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of T Mu Sent: Thursday, 17 August 2006 3:23 a.m. To: R-Help Subject: [R] confusing about contrasts concept Hi all, Where can I find a thorough explanation of Contrasts and Contrasts Matrices? I read some help but still confused. Thank you, Tian [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ The contents of this e-mail are privileged and/or confidential to the named recipient and are not to be used by any other person and/or organisation. If you have received this e-mail in error, please notify the sender and delete all material pertaining to this e-mail. __ [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Setting contrasts for polr() to get same result of SAS
Hi all, I am trying to do a ordered probit regression using polr(), replicating a result from SAS. polr(y ~ x, dat, method='probit') suppose the model is y ~ x, where y is a factor with 3 levels and x is a factor with 5 levels, To get coefficients, SAS by default use the last level as reference, R by default use the first level (correct me if I was wrong), The result I got is a mixture, using first and last for different variables. I tried relevel, reorder, contrasts, but no success. I found what really matters is options(contrasts = c(contr.treatment, contr.poly)) or options(contrasts = c(contr.SAS, contr.poly)) so I guess I can set contrasts= a list of contrasts for each variables in polr(), but I cannot successfuly set the contrasts, what is the syntax? Is there a better way to do this? Thank you very much, Tian [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] How to show classes of all columns of a data frame?
Hi all, Suppose I have a data frame myDF, col A is factor, col B is numeric, col C is character. I can get their classes by class(myDF$A) but is there a quick way to show what classes of all columns are? Thank you. Tian [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] model = F causing error in polr()
Hi all, I got an error message if I set model =F in polr(), like polr(y ~ x1 + x2, data1, model = F, method = probit) Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for '(model)') but polr(y ~ x1 + x2, data1, method = probit) would work. Why? Thank you, Tian [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] coefficients' order in polr()?
Hi all, I am using polr(). The resulting coefficients of first levels are always 0. What to do if I wnat to get the coefficients of the last level 0. For example, suppose x has 3 levels, 1, 2, 3 probit - plor(y ~ x, data1, method='probit') will get coefficients of level 2, 3 of x, but I want coefficients of level 1, 2 Thank you, Tian [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] help: cannot allocate vector of length 828310236
Hi all, I was trying a probit regression using polr() and got this message, Error in model.matrix.default(Terms, m, contrasts) : cannot allocate vector of length 828310236 The data is about 20M (a few days ago I asked a question about large file, thank you for responses, then I use MS Access to select those columns I would use). R is 2.3.1, Windows XP, 512M Ram. I am going to read some help on memory use in R, but hope anybody can give me some quick hints. Is it because iphysical memory runs out, or some other things could be wrong with data or polr()? Does R use virtual memory? If so, what options can I set? If not, can R deal with really huge data (except adding RAM according to data size)? If this is the case, it is too bad that I have to tell my boss to go back to SAS. Now it is not a speed issue yet. Thank you. [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] problem in reading large files
I was trying to read a large .csv file (80 colums, 400,000 rows, size of about 200MB). I used scan(), R 2.3.1 on Windows XP. My computer is AMD 2000+ and has 512MB ram. It sometimes freezes my PC, sometimes just shuts down R quitely. Is there a way (option, function) to better handle large files? Seemingly SAS can deal with it with no problem, but I just persuaded my professor transfering to R, so it is quite disappointing. Please help, thank you. [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
