search for: modls

Hello there, I would like to test the agreement amongst 6 raters for nominal data on a scale from 1-4, and conduct a latent class analysis in R. How should the data be formatted and what code should I use? Thank you very much Lisa Wang Princess Margaret Hospital Biostatistics tel:416 946 4501

Hello, In doing a series of non-linear estimations of a function which is a sum of a varying number of sinusoids, I would like to "autogenerate" the arguments needed by nls() depending on that number. For example, when there are two sinusoids: > nls( y ~ mu + A1 * cos(2*pi*f1*x - P1) + A2 * cos(2*pi*f2*x - P2), data = some.xy.data, start = list( mu=some.value0,

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

This might be an easy question. I tried to catch the results of nls, but to no avail. For example, if I try to fit a modle y~A*x*x+B*x+C nls will print out what A, B, and C, but how can I store these numbers to a different variable so that I can make use of them? I tried to "unclass" the result of nls, but couldn't see any of the parameters. Y C Tao

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

...4 --------------------------------------------------------------------------- Count Model (Negative Binomial) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 7.316434 0.217397 33.6547 2.659e-248 modes -0.329699 0.363943 -0.9059 3.650e-01 modls 0.451599 0.267710 1.6869 9.162e-02 modmag 1.713005 0.296333 5.7807 7.440e-09 modpag 1.654261 0.266674 6.2033 5.529e-10 modsl 1.946231 0.308218 6.3145 2.711e-10 modacpas 2.379625 0.275172 8.6478 5.252e-18 intact -0.0...

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

Hello, I've configured a squid proxy server to use the wb_auth module to authenticate NT Users . I'm aware that this is not the squid users mailing list but since the wb_auth modle uses the winbind application i thought this might be a good place to look for some help. Basically, the wb_auth module asks winbind to authenticate a users. Winbind then returns a '0' if the

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,

Dear Sir I am new user of R. I am interested in modeling hydrological extreme events. I found MSClaio2008 very interesting function. In this function four criterions for choosing distributions. Can we call these criterions as model selection techniques or goodness of fit techniques or both? Because goodness of fit techniques are usually performed after modle selection. Can I found

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

I ran "apt-get -b source zaptel-source" to download and compile the zaptel source. It successfully compiles and builds the following packages: libtonezone-dev_1.0.2-2_i386.deb libtonezone1_1.0.2-2_i386.deb zaptel-source_1.0.2-2_all.deb zaptel_1.0.2-2_i386.deb None of them contain the kernel modules. Is there a way I can get it to compile the kernel modules? Thanks, Geoff

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