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Hello to all, I am having trouble with intregrating a complicated uni-dimensional function of the following form Phi(x-a_1)*Phi(x-a_2)*...*Phi(x-a_{n-1})*phi(x-a_n). Here n is about 5000, Phi is the cumulative distribution function of standard normal, phi is the density function of standard normal, and x ranges over (-infty,infty). My idea is to to use quadrature to handle this integral. But

Hi folks, I have a question about how to efficiently produce random numbers from Beta and Binomial distributions. For Beta distribution, suppose we have two shape vectors shape1 and shape2. I hope to generate a 10000 x 2 matrix X whose i th rwo is a sample from reta(2,shape1[i]mshape2[i]). Of course this can be done via loops: for(i in 1:10000) { X[i,]=rbeta(2,shape1[i],shape2[i]) } However,

So your question is about fitting a regression model for all the subsets of predictors? Then there would be 2^13 submodesl? Probably leaps() does what you want. This function does a all-subset regresion. -- View this message in context: http://r.789695.n4.nabble.com/Regression-Models-tp4173278p4180447.html Sent from the R help mailing list archive at Nabble.com.

Dear folks, I have a question about the image() function in R. I found the following link talking about this but the replies didn't help with my situations. http://r.789695.n4.nabble.com/question-on-image-function-td839275.html#a839276 To be simple, I will keep using the example in the above link. Suppose the data are like x y mcpvalue 0.4603578

Hi folks, I am having a question about efficiently finding the integrals of a list of functions. To be specific, here is a simple example showing my question. Suppose we have a function f defined by f<-function(x,y,z) c(x,y^2,z^3) Thus, f is actually corresponding to three uni-dimensional functions f_1(x)=x, f_2(y)=y^2 and f_3(z)=z^3. What I am looking for are the integrals of these three

Dear all, I am having trouble with the following problem. Suppose we have the fourth order ODE with boundary conditions: http://r.789695.n4.nabble.com/file/n4591748/problem.jpg problem.jpg where q(t) is a known function. Note here the lambda parameter is changing, so essentially we have a series of ODEs. lambda is called an eigenvalue, the solution y is called an eigenfunction associated with