search for: sample_n

...a 6 but it is a Trial, so I would like use R intead (or better, I need it)! To exemplify, In Mathematica I call the function using NMinimize passing the restriction and parameters: /* function name "findOpt" and parameters... */ restriction = (1 - alpha) <= CDF[BinomialDistribution[sample_n, p1], c] && betha >= CDF[BinomialDistribution[sample_n, p2], c] && 0 < alpha < alphamax && 0 < betha < bethamax && 1 < sample_n <= lot_Size && 0 <= c < lot_size && p1 < p2 < p2max ; fcost = sam...

...respect to one of the points. I?m using Mathematica 6 but it is a Trial, so I would like use R intead (better, I need it)! In Mathematica I call a parameter called restriction: // fucntion name "findOpt" and parameters... restriction = (1 - alpha) <= CDF[BinomialDistribution[sample_n, p1], c] && betha >= CDF[BinomialDistribution[sample_n, p2], c] && 0 < alpha < alphamax && 0 < betha < bethamax && 1 < sample_n <= lot_Size && 0 <= c < amostra && p1...

...a 6 but it is a Trial, so I would like use R intead (or better, I need it)! To exemplify, In Mathematica I call the function using NMinimize passing the restriction and parameters: /* function name "findOpt" and parameters... */ restriction = (1 - alpha) <= CDF[BinomialDistribution[sample_n, p1], c] && betha >= CDF[BinomialDistribution[sample_n, p2], c] && 0 < alpha < alphamax && 0 < betha < bethamax && 1 < sample_n <= lot_Size && 0 <= c < lot_size && p1 < p2 < p2max ; fcost = sam...

I have an advanced question about bootstrapping. There are two datasets. In each bootstrap iteration, I would like to sample One observation per cluster from the first dataset. N observations with replacement from the second dataset. Right now I am using dplyr::sample_n() for first dataset, with this sampling embedded in the program that boot() from the boot package is running to sample the second dataset and produce the estimates. I would prefer to do the entire sampling in the boot() part as opposed to embedding the sample_n() statement. The reason is so that...