Hallo Thomas Thank you for your answer, even I am not sure how to do it in R (or maybe at all). My mathematics background is only faint so I drop the first possibility which is for me rather cryptic.
Does your second suggestion mean: 1: compute random variable y <- f(rnorm(n,mymeanx1,mysdx1), rnorm(n,mymeanx2, ...), ...) according to my function f (based on assumption x variables values can be considered normally distributed and and independent) 2: sd(y) can be considered as variation of y? Or is it necessary to do something like vysled<-NULL for (i in 1:300) vysled[i]<-sd(sample(y,100)) mean(vysled) to get bootstraped estimation of sd(y) My actual data have some missing values and some outliers which I can either remove or to use some robust statistics for mean and variation estimates. Thank you and have a nice Christmas Petr On 22 Dec 2003 at 8:56, Thomas W Blackwell wrote: > Petr - > > Very briefly, I think of three ways to approximate the standard > deviation of y = f(x1,x2,x3). > > (1) linearise f() and use the covariance matrix of [x1,x2,x3]. > (2) simulate draws from the joint distribution of [x1,x2,x3], > then compute the sample std dev of resulting f()s. > (3) go back to the original data set from which [x1,x2,x3] were > estimated as parameters, re-sample rows with replacement, > estimate [x1,x2,x3] and compute f, then take sample std dev. > > Other names for these three would be (1) the "delta method" or > Taylor series expansion, (2) parametric bootstrap, (3) bootstrap. > > Different choices are appropriate in different situations. > > If the std devs of x1,x2,x3 are small relative to the curvature > (2nd derivative) in f(), then use (1) and compute by matrix algebra > > Var(f(x1,x2,x3)) approx t(grad f) %*% Cov(x1,x2,x3) %*% grad f. > > If the curvature in f() is an issue, use (2) with draws of x1,x2,x3 > from some parametric distribution (eg, rnorm()) with each component > properly conditioned on the ones already drawn. > > Only if there were no set of intermediate parameters [x1,x2,x3] > would I use (3) to get the precision of f directly. I'm sure > Brad Efron would say something different. (3) is the only one > that is canned in R, simply because the other two are practically > one-liners. > > - tom blackwell - u michigan medical school - ann arbor - > > On Mon, 22 Dec 2003, Petr Pikal wrote: > > > Dear all > > > > Please, can you advice me how to compute an error, standard > > deviation or another measure of variability of computed value. > > > > I would like to do something like: > > > > var(y) = some.function(var(x1),var(x2),var(x3)) > > > > for level F1 (2,3,...) > > > > Let say I have some variables - x1, x2, x3 (two computed for levels > > of factor F and one which is same for all levels) and I want to > > compute > > > > y = f(x1,x2,x3) > > > > for some levels of factor F > > > > I can compute variation of variables for levels of F, I know a > > variation of one variable but I am not sure how to transfer it to > > variation of y within respective levels. > > > > I found some methods which I can use but I wonder if there is some > > method implemented in R (Manly B.F. Biom.J.28,949,(1986), some local > > statistical books available to me). > > > > I have a feeling I could use bootstrap method for this but I am not > > sure how. > > > > Thank you and merry Christmas to all > > > > Petr Pikal > > [EMAIL PROTECTED] > > > > ______________________________________________ > > [EMAIL PROTECTED] mailing list > > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > > > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help Petr Pikal [EMAIL PROTECTED] ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
