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Hello, I want to create a matrix of N random numbers with a uniform distributions. Later, I want to repeat T times each row of this matrix. For this I do the following loop: N<-45 T<-10 n<-N*T a<-matrix(runif(N,min=-1,max=1),nr=N) mymat<-matrix(rep(NA,n),nr=n,nc=1) for(i in i:N){ b<-rep(a[i,],T) mymat[i,]<-b } Mi problem is that with this loop I can see the output of the

Hello, I want to create two random variables (x1,x2) both with uniform distribution bounded by (-1) and (1) that has a correlation of 0.6 between them. Does somebody know how I can do it? For normal random variables I known how to implement it with the rmvnorm command but I don't know how to do it with variables uniformly distributed. Thanks a lot. Alexandra [[alternative HTML

Dear memberships, I'm trying to estimate the following multivariate local regression model using the "locfit" package: BMI=m1(RCC)+m2(WCC) where (m1) and (m2) are unknown smooth functions. My problem is that once I get the regression done I cannot get the fitted values of each of this smooth functions (m1) and (m2). What I write is the following library(locfit) data(ais)

Hi, I want to calculate the bandwith of a multivariate local polynomial regression such as the model is: Y=m(X,Z)+u. Please, somebody knows how I can do it in a multivariate case? In an univariate case I use the instrucction "regband" of the locfit package, but it is impossible to find a way to do it in the bivariate case. Please, help me. Thanks you in advance Alexandra

Hello to everybody, Somebody know how I can apply the instruction (regband) of the package (locfit) to a multivariate covariate. In the instructions states that the formula has to have only one predictor. How can I use that when I have an unknown function with two predictors, m(X,Z)? Is there any other way to get a bivariate bandwidth in a local regression polynomial? I'll appreciate any

Dear memberships, I'm trying to estimate the following multivariate local regression model using the "locfit" package: BMI=m1(RCC)+m2(WCC) where (m1) and (m2) are unknown smooth functions. My problem is that once I get the regression done I cannot get the fitted values of each of this smooth functions (m1) and (m2). My program is the following: library(locfit)