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Dear R developers, I am a little disappointed that my bug report only made it to the wishlist, with the argument: Well, it does not say it has. Only relevant to prediction intervals. predict.lm does calculate prediction intervals for linear models from weighted regression, so they should be correct, right? As far as I can see they are bound to be wrong in almost all cases, if no weights

...t;) all.perm_function(n){ p_matrix(1,ncol=1) for(i in 2:n){p_pp_cbind(p,i) v_c(1:i,1:(i-1)) for(j in 2:i){v_v[-1] p_rbind(p,pp[,v[1:i]])}} p} Until now I hesitated to dwell on writing functions to generate all n!/(k!(n-k)!) k-combinations of an n-element set or more generally all n!/(k_1!k_2!...n_k!) choices of placing n objects into k boxes where box j has n_j objects. Not intending to re-invent the wheel I turn to the R community to ask if someone has already written these functions. Any help is thankfully appreciated. --- D.Trenkler --- **********************************************...

Greetings, I have the following? Problem: Given k (=10) discrete independent random variables X_i with n_i (= 5 to 20) values each,compute quantiles of the distribution of the sum X = X_1+...+X_k. Here X has n=n_1 x n_2 ... n_k distinct values which is too large to list them all together with their probabilities. I tried several approaches: (A) Convolution: each X_j is approximated with Y_j=X_j+Z, where Z is an N(0,sigma) variable with small sigma. Then Y_j is a probability mixture of the normal variables N(x_j,sigma...

...sh.console(); print(D[p1]); myReject.pooled = myReject.pooled.1 = MAX.pooled = rep(-1,nsim); gsim = 0; ## good simulations for(i in 1:nsim) { doubleBreak = F; print(paste(i, " of ",nsim)); flush.console(); tData = NULL; pooledNum = matrix(0,nrow=k,ncol=k); ##numerator as weighted sum AS (n_k-1)cov.scaled pooledDen = 0; ##denominator as correction AS N-k #Sigma_pooled = ((omit.1-1)*summary.nls.1$cov.scaled + (omit.2-1)*summary.nls.2$cov.scaled + (omit.L-1)*summary.nls.L$cov.scaled)/(sum(omit.1,omit.2,omit.L)-L); for(j in 1:L) { Y = numeric(N_i); X = createDomain(Xval,N_i); noise = rn...