similar to: ks.test for conditional distribution Y|x

Dear experts! Is there a package that enables to create the bivariate log-normal variables? Thanks a lot, Vicky Landsman. [[alternative HTML version deleted]]

Following the recommendation of Prof. Ripley, I have created the Makevars file with the line: PKG_LIBS="-L/usr/lib/libm -lm -L/usr/local/lib/libgsl -lgsl -L/usr/local/lib /libgslcblas -lgslcblas" in the working directory. Now I have the code file Example3.c which computes the Bessel function value (the example is taken from the GSL reference book). I am running: R CMD SHLIB Example3.c

Dear all, I'd like to find a minimum of (-loglik) function which is a function of k parameters. I'd like to run the minimization algorithm for the different subsets of the parameters and assign the fixed values to the complementary subset. How should I define my (-loglik) function such that it can be passed to the optim or other optimization function? Much thanks for any suggestions.

>From the `R Data Import/Export' manual, shipped with R, as well as available from the official R web site (last three paragraphs of Section 3.1, describing functions in the `foreign' package): Function read.S which can read binary objects produced by S-PLUS 3.x, 4.x or 2000 on (32-bit) Unix or Windows (and can read them on a di erent OS). This is able to read many but not all S

Dear experts! I have a minimization problem with non-linear constraint and Objective function(theta)=lambda*(Constr)^2-f(x,theta). Theta is a vector of parameters. I'd like to stop the optimization after the value of the constraint is less or equal some constant value, say d, and save the last computed value of the function. For this purpose, I thought to define the Objective function like

Dear all, I think that my question is very simple but I failed to solve it. I have a list which elements are matrices like this: >mylist [[1]] [,1] [,2] [,3] [1,] 1 3 5 [2,] 2 4 6 [[2]] [,1] [,2] [,3] [1,] 7 9 11 [2,] 8 10 12 I'd like to create a matrix M<-mylist[[1]]+mylist[[2]] [,1] [,2] [,3] [1,] 8 12 16 [2,] 10 14 18

Dear all, I need an advice in the following problem. I have to maximize two functions of the form f1(x)=f(y1,x,alpha1,beta1) and f2(x)=(y2,x,alpha2,beta2), the maximization is with respect to alpha1, alpha2, beta1, beta2. I can maximize each function separately using nlm. The problem is that I have to add the constraint of the form g(alpha1)=g(alpha2). The total number of parameters is

This is not a direct answer to your question, but if all you want to do is integrate a function numerically, you don't need to be doing it through R. You should be able to find codes that you can use directly, such as quadpack or even GSL. You may want to search on GAMS (Guide to Available Mathematical Software, http://gams.nist.gov/). Andy > From: Victoria Landsman > > Dear all,

I have found the use of save( ) and attach( ) when supported by a pair of functions written by my colleague John Miyamoto, move( ) and rm.sv( ) quite useful in managing (1) collections of useful homebrew functions, (2) project workspaces, and (3) "packages" under development. An .Rdata file containing these and other handy functions together with a brief supporting document can be

Deall all, We need to perform a statistical analysis of a large database (40,000 entries with approximately 500 fields in each entry) currently handled in Oracle. The data contains categorical variables only. At the current stage we suggest classification and clustering analysis. We are planning to perform the analysis in R and would be very grateful for any

Dear all, I'd like to find the mle esttimates using the mle function mle(negloglik, start = list(), fixed=list(), method="..."). I am using the L-BGFS-B method and I don't supply the gradient function. Is there a way to print the gradients found at the solution value? I am using R-1.9.1 on Windows and on Unix. Thank you in advance, Victoria Landsman. [[alternative

Full_Name: Andrew Grant McDowell Version: R 1.1.1 (but source in 1.3.0 looks fishy as well) OS: Windows 2K Professional (Consumer) Submission from: (NULL) (194.222.243.209) In article <xeQ_6.1949$xd.353840@typhoon.snet.net>, johnt@tman.dnsalias.com writes >Can someone help? In R, I am generating a vector of 1000 samples from >Bin (1000, 0.25). I then do a Kolmogorov Smirnov test

Hello all, A friend just showed me how ks.test fails to work with pbinom for small "size". Example: x<-rbinom(10000,10,0.5) x2<-rbinom(10000,10,0.5) ks.test(x,pbinom,10,0.5) ks.test(x,pbinom,size = 10, prob= 0.5) ks.test(x,x2) The tests gives significant p values, while the x did come from binom with size = 10 prob = 0.5. What test should I use instead ? Thanks, Tal

Dear all, I need to compute (numerically) the two-dimensional integral: int(int f(x,y)dy)dx. What is the more efficient(fast) way to do it? Is adapt function appropriate for this problem? I will much appreciate your help and attention. Vicky. [[alternative HTML version deleted]]

Hello, I'm trying to test my data for normality. I enter the data (95ish species counts) run >ks.test (data,pnorm) and get a p- value <2.2e-16 But this seems to be the p-value no matter what the data I enter. (I have multiple datasets and am testing them all for normality). [Actually, I just entered a vector of 1's and the p-value changed.] When I use the >Shapiro.test command,

> x <- runif(100) > y <- runif(100) > ks.test(x,y) Two-sample Kolmogorov-Smirnov test data: x and y D = 0.11, p-value = 0.5806 alternative hypothesis: two-sided ok I expected that, but: > ks.test(runif(100), "runif") One-sample Kolmogorov-Smirnov test data: runif(100) D = 0.9106, p-value < 2.2e-16 alternative hypothesis: two-sided How

Hi, I am performing GEV analysis on temperature/precipitation data and want to use the ks.boot function but I am unsure of how to implement it using the pgev distribution. For example: ks.test(data,pgev,shape,location,scale) ks.boot(data,(distribution parameters?),alternative="pgev",nboots=1000) Any advice? Apologies in advance if I have used the wrong email address. Regards, Louise

Dear All, I defined a distribution and I want to test it by k-s test? How to input parameters for ks.test(.....)? E.g., F(x,m) is user-defined function x is observation value, m is parameter of F y is observation vector value in experiment > ks.test(y,"F",m=0.2) error.... why? ' -- Best wishes, Zhijiang Wang -------------------------------------------- PHD Student Room

It's the "|source" in your formula that tells lattice to separate them. If you drop that, you'll get all points without S and P distinguished at all. If you add a groups argument, you should get them presented with different colors/symbols/etc. depending on your trellis settings (warning: untested code): par.plot(lnvol~lnden, groups =

Hello, I want to do normality test on my data I write this but I don't understand the display of the results ks.test(data,"pnorm") In fact I want to know if my data is a normal distribution. I have to check the p-value or D? Thanks. _____________________________________________________________________________ l [[alternative HTML version deleted]]