similar to: manipulating text to generate different formulas to use in nls()

hi all, i've been struggling with setting appropriate rules for an SMTP-server behind by NAT'd firewall. it's not that there is too little info on the web -- or here, for that matter -- there's scads of it for seemingly endless configs/req'ts -- none that seem to be exactly my own. bottom line: i'm a bit confused, and looking for some experienced advice. my goals (for

Hi all R users Ive got a file that contains diffrent settings in the manor of: setting1="value1" setting2="value2" setting3="value3" setting4="value4" . . . What I want to do is open the file and change the value of a specific setting like wanna change setting4="value4" -> setting4="value5" and then save the file again.

I have a data frame with a couple million lines and want to retrieve the largest and second largest values in each row, along with the label of the column these values are in. For example row 1 strongest=-11072 secondstrongest=-11707 strongestantenna=value120 secondstrongantenna=value60 Below is the code I am using and a truncated data.frame. Retrieving the largest value was easy, but I have

I await Luke''s node settings implementation with interest. At the moment however, I have this sort of ugliness: $site = $hostname ? { fred => "opsera", barney => "bedrock", default => "unknown site", ... } So I''ve knocked up this little function to use CSV files instead. Now I can just do: $site =

Hi all, After writing data in MMF(Memory Map File) using SEXP structure, can i reference in R? If input data is larger than 2GB, Can i reference MMF Data in R? my work environment : R version : 2.11.1 OS : WinXP Pro sp3 Thanks and best regards. Park, Young-Ju from Korea. ---------[ ???????? ???????? ???????? ]---------- ???????? : R-help Digest, Vol 89,

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I have a module having the blockaddress instruction. When I link it into another module and delete the original, blockaddress disappears and is replaced by inttoptr (i32 1 to i8*). Please compile and run the attached program to see the demo of this problem. Right after linking modules, blockaddress still exists: @switch.bbs = internal global [3 x i8*] [i8* blockaddress(@my_func,

I am caught in a mental trap. Why isn't the between groups variance estimated (0.0038) to be around the value with which I generated the data (0.0002)? Thanks Toby set.seed(76589437887) fph = 0.4 Sigh = sqrt(0.0002) Sigi = sqrt(0.04) ci = 1 fpi = matrix(,7200,3) for (i in 1:90) { fph = rnorm(1, fph, Sigh) for (k in 1:80) { fpi[ci,1:3] = matrix(c(i, k, rnorm(1, fph, Sigi)),1) ci

Anyone know how to get p-values for the t-values from the coefficients produced in vglm? Attached is the code and output ? see comment added to output to show where I need p-values + print(paste("********** Using VGAM function gamma2 **********")) + modl2<- vglm(MidPoint~Count,gamma2,data=modl.subset,trace=TRUE,crit="c") + print(coef(modl2,matrix=TRUE))

Simple usage questions that I ought to be able to figure on my own, but can't. 1.I'm able to produce a cross tabulation table showing counts with either table or xtabs. But I want column percentages for interpretation, and it seems stupid to sit there with a calculator figuring marginals and column percentages. How to make R do it after this: > x <- c(1,3,1,3,1,3,1,3,4,4)

We want to exclude virtio-gpu since it's irrelevant, large, and pulls in other unwanted dependencies (modeswitching, drm). Add a second list of kmods which is a blacklist, applied after the first. This reduces the libguestfs initrd size from 39M down to 17M, with concomitant small reductions in boot time. --- src/ext2_initrd.ml | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-)

Question: after fitting a gamma function to some data, how do I get predicted values? I'm a SAS programmer, I new R, and am having problems getting my brain to function with the concept of "object as class ...". The following is specifics of what I am doing: I'm trying to determine the pdf from data I have created in a simulation. I have generated frequency counts

Hello, I want to generate data set from Cox PH model with gamma frailty effects. theta(parameter for frailty distribution)=2 beta=1.5 n=300 cluster size=30 number of clusters=10 I think I should first generate u from Gamma(Theta,theta) and then using this theta I could not decide how I should generate the survival times? Is there any package for this? or any document you could suggest? Any

Hi, few cleanups in the supermin codebase; no actual functional change. Thanks, -- Pino Toscano (3): ext2: simplify tracking of visited modules utils: remove unused run_python function Add and use an helper error function src/build.ml | 20 +++++----------- src/dpkg.ml | 4 +--- src/ext2_initrd.ml | 10 ++++---- src/kernel.ml | 27

https://bugzilla.mindrot.org/show_bug.cgi?id=2500 Bug ID: 2500 Summary: ConnectionAttempts=0 causes ssh to output uninitialised data on stdout Product: Portable OpenSSH Version: 7.1p1 Hardware: amd64 OS: Linux Status: NEW Severity: normal Priority: P5 Component: ssh

Hi James, I have two questions for you. Firstly, what is the role of 'module' in init.cc? I can see that its being treated like it is a 'bytecode' file, as we open it and then pass it to the ByteCoder and eventually construct llvm module from it. Like In file init.cc, line:121 FILE *stream = fopen("/module", "rb"); ... fread(c, 1, sz,

When building the mini initrd, previously we copied the modules into the initrd as-is, so for example if the module was xz-compressed, we copied the foo.ko.xz file to the initrd. This requires that the mini init binary is linked to zlib & lzma, so that it knows how to uncompress these modules when insmoding them at boot time. Also since the init is statically linked, it required _static_

Please I need some help using R to analyze my data. What I would like to do is to repeat the same basic process (e.g. linear regression between wood density and distance from pith) for at least 240 data subsets within the main data-frame. Within the main data-frame, these data subsets will be defined by three variables namely,  species, individual and core (i.e. 20 species, at least 6

Hi Mian, Looking at the runlog, everything seems fine until LLVM attempts to use lseek() on a file. You see the PANIC because Horizon hasn't implemented lseek yet. Obviously the version of GlibC I was using does not use lseek in that circumstance, but yours does. You just need to implement lseek :) Cheers, James -----Original Message----- From: Mian M. Hamayun

Allow an alternate libc, such as dietlibc, to be used for the init binary in the supermin appliance. Rich.