Aimee Kopolow <alj27 <at> georgetown.edu> writes: > > Hi all, > > I am attempting to use latin hypercube sampling to sample different > variable functions in a series of simultaneous differential equations. > There is very little code online about lhs or clhs, so from different > other help threads I have seen, it seems I need to create a > probability density function for each variable function, and then use > latin hypercube sampling on this pdf. > > So far, I have created a data frame consisting of the "y" output of > density(functionX) for each of the different functions I wish to > sample. [examples of functions include T1(t), WL1(T1,t), > BE1(WL1,T1,t)] The dataframe consists of 512 rows/vectors for each > function. > > I tried running > res <- clhs(df, size = 500, iter = 2000, progress = FALSE, simple = TRUE) > > and it returned a single series of 500 samples, rather than a series > of 500 samples per function. > > I ultimately need a sample of each variable function that I can run > through my model, putting each individual variable function as a > constant instead, and then performing PRCC. Is there anyone who can > advise on how to do this, or failing that, where I should look for > sample code? > > Thank you for any help you are able to give, > Aimee. > >
Aimee, I'm the package maintainer for the lhs package. Unfortunately, I'm not familiar with the functions you mentioned (reproducible code would help us answer your post). I will try to show something parallel to what you described. require(lhs) # functions you described T1 <- function(t) t*t WL1 <- function(T1, t) T1*t BE1 <- function(WL1, T1, t) WL1*T1*t # t is distributed according to some pdf (e.g. normal) require(lhs) # draw a lhs with 512 rows and 3 columns (one for each function) y <- randomLHS(512, 3) # transform the three columns to a normal distribution (these could be any # distribution) t <- apply(y, 2, function(columny) qnorm(columny, 2, 1)) # transform t using the functions provided result <- cbind( T1(t[,1]), WL1(T1(t[,2]), t[,2]), BE1(WL1(T1(t[,3]), t[,3]), T1(t[,3]), t[,3]) ) # check the results # these should be approximately uniform windows() par(mfrow=c(2,2)) apply(y, 2, hist, breaks=50) # these should be approximately normal windows() par(mfrow=c(2,2)) apply(t, 2, hist, breaks=50) # these should be the results of the functions windows() par(mfrow=c(2,2)) apply(result, 2, hist, breaks=50) Please feel free to contact me as the package maintainer if you need additional help with lhs. Rob ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
