search for: minimazing

Hello I want to ask if the estimator method LTS (Least Trimmed Squares) is implemented in R. I've found the lqs(y~x,method = c("lts")) tool that implements LTS but minimazing the sum of the `quantile' smallest squared residuals. I don't know if this is the same as the clasical LTS, if it is, where do I set the trim (h value to trim the LS sum)? I'll be waiting for any help. Thanks for listening Alpha ___________________________...

Hi, I'm using R to find esplicit functions fitting set of data. The data contains about 30 points, which have different weights (number of cases represented from the point). I plot the points, choose "by eye" a function made with exp or arctg or polinomial and use nlm to minimaze the root mean error with correct the weights. For Example: Err <- function(p) sum((weight*(y

Hi list, I have a group of data. It looks like they follow a exponential distribution. In R, how can I esimate lamda, that is the rate in pexp, of the distribution and can I use Kolmogorov-Smirnov for hypothesis testing in such a situation? I have read the "8.2 Examing the distribution of a set of data" of "An Introduction to R" but I did not find any clues on this issue.