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Dear list members, I am trying to estimate parameters of the AR(1)-GARCH(1,1) model. I have one additional dummy variable for the AR(1) part. First I wanted to do it using garchFit function (everything would be then estimated in one step) however in the fGarch library I didn't find a way to include an additional variable. That would be the formula but, as said, I think it is impossible to add

Hi everybody, I'm trying to fit a Garch(1,1) process to the DAX returns. My data consists of about 2300 10day-logreturns in chronologically descending order (see attachment). But if I use the garch function I get a very high alpha_1 and a quite low beta, which doesn't make that much sense. I think I am missing something, but have no idea what it might be. I'd appreciate it a lot

Dear R-Friends, How can I calculate a double integral like \int_a^b \int_c^y g(x, y) dx dy where a, b, c are constants, g(x, y), e.g., g(x, y) = tan(x + y). I tried to nested integrate() and adapt(), but none of them working, seemingly due to the limits can not be specified constants. Best regards, C. Joseph Lu Department of Statistics National Cheng-Kung University Voice:

[apologies for possible multiple post] Hi The manpage for integrate() does not mention imaginary numbers. I found the following unexpected: > integrate(function(x){0*x+1+1i},0,1) 1 with absolute error < 1.1e-14 > (I would expect 1+1i here) One can write a little wrapper, but it's not straightforward. Would it be hard to accommodate such functions in integrate()? Some other

I'm hoping someone with some experience generating tables in Sweave will be able to solve this problem for me. I'm experiencing some inconsistency with the way floating point numbers are displayed -- for some tables, they will be in standard notation, e.g. 11±26 while in others, it uses exponential notation, e.g. 1.1e+01±2.6e+01 I am using code such as the following; note I have

Hi.. i was trying to integrate the indicator funtion but had problems when limits where negative or equal to the indicator condition my function is ________________________ fun1<-function(x){ as.numeric(x>=2) } _________________________ which should be Ind(x>=2)*x seems to work for the following two cases

I've had a very long file written out by R with write.table, with fields of time values, converted from POSIXlt as.numeric. Among 2.5 million values, very few had 6 trailing zeroes, and those were output in scientific notation as in the subject. Is this the default behavior for long integers, and how can it be turned off (with all digits for any integer field in write.table)? This

Dear all, I'm stuck on a problem concerning integration..Results from the analytical expression and numerical approximation (as returned by integrate()) do not match. It probably depends on some error of mine, so apologizes for this off-topic question. I'm interested in computing the integral of f where: f<-function(x){exp(-3-.2*pmin(x-7.5,0))} x<-seq(0,15,length=50) plot(x,

I'm having a function of the form 1> f<-function(x){ 1+ 1+ return(x^p) 1+ 1+ } ,and I would like to integrate it with respect to x, where p should be any constant. One way would be to set a value for p globally and then call integrate function: p=2 integrate(f, lower = -1, upper = 1) However, I would like to use 'integrate' inside a function, so I could call it passing

Hi, Suppose I have: 459 1789 23590 and I'd like to round them to: 400 1700 24000 On the other hand, say if I have: 232 1234 23120 that need to be rounded to: 300 1300 24000 I tried the round(), floor() or ceiling() and can't get what I want. Is there any tricks I can use to achieve this goal? Cheers, Kevin

hi all- I am doing some research, have never used R before until today and need to understand the following program for a project. if some one could PLEASE help me understand this program ASAP i would GREATLY appreciate it (any syntax/ statistic comments would be great) PLEASE PLEASE HELP!! THANKYOU!!! -on a side note, it seems to me that R doesnt include the pv, and it was calculated

Dear R-users, I need to use the aftreg function in package 'eha' to estimate failure times for left truncated survival data. Apparently, survreg still cannot fit such models. Both functions should be fitting the accelerated failure time (Weibull) model. However, as G?ran Brostr?m points out in the help file for aftreg, the parameterisation is different giving rise to different

virtqueue_add() populates the virtqueue descriptor table from the sgs given. If it uses an indirect descriptor table, then it puts a single descriptor in the descriptor table pointing to the kmalloc'ed indirect table where the sg is populated. Previously vring_add_indirect() did the allocation and the simple linear layout. We replace that with alloc_indirect() which allocates the indirect

Hi, I am having trouble with the survival library, particualrily the coxph function. the following works coxph(jtree9$cph.call,z,rep(1,dim(z)[1])) Call: coxph(formula = jtree9$cph.call, data = z, weights = rep(1, dim(z)[1])) coef exp(coef) se(coef) z p SM 0.2574 1.294 0.0786 3.274 1.1e-03 Sex -0.1283 0.880 0.1809 -0.709

Dear list members, I'm quite new to R, and though I tried to find the answer to my probably very basic question through the available resources (website, mailing list archives, docs, google), I've not found it. If I try to use the "integrate" function from within my own functions, my functions seem to misbehave in some contexts. The following example is a bit silly, but

I resurrected these patches after prompting from Andy Lutomirski's recent patches. I put them on the back-burner because vring_bench had a 15% slowdown on my laptop: pktgen testing revealed a speedup, if anything, so I've cleaned them up. Rusty Russell (3): virtio_net: pass well-formed sgs to virtqueue_add_*() virtio_ring: assume sgs are always well-formed. virtio_ring: unify

I resurrected these patches after prompting from Andy Lutomirski's recent patches. I put them on the back-burner because vring_bench had a 15% slowdown on my laptop: pktgen testing revealed a speedup, if anything, so I've cleaned them up. Rusty Russell (3): virtio_net: pass well-formed sgs to virtqueue_add_*() virtio_ring: assume sgs are always well-formed. virtio_ring: unify

1. There are updates to CentOS-4Beta for the i386 and x86_64 arches. The following RPMS have been changed: a. createrepo-0.4.2-1.noarch.rpm - This is an update from the upstream maintainer. b. yum-2.1.13-1.c4.noarch.rpm - This is an update from the upstream maintainer. c. firefox-1.0-6.centos4.3.i386.rpm - The original build did not strip the library files of unnecessary symbols, causing

Hi I'm having some difficulty understanding my output (below) from GEE. the person who wrote the program included some comments about the '3-th term gives diff between hyp/ox at time..', and after created an L vector to use for a WALD test. I was wondering if someone could help me understand the GEE output, the programmers comment, how L was determined, and its use in the WALD

Dear all, I have a question with respect to counting process formulation of the coxph(survival) model. I have two groups of observations for which I have partitioned each observation into two distinct time intervals, namely, entry day till day 13, and day 13 till death or censorship day (of course the latter only for the observations that survived the first 13 day interval), and added a