Now we move on to the second part of the Exercise 5.2, which requires to implement regularized logistic regression using Newton’s Method. Plot the data:
Posts Tagged ‘ Algorithm ’
Algorithm Whisperer
I was staring at my favorite trading algorithm the other day and I could swear it wanted to tell me something. I've made contact with it in the past, but our conversations pretty much ran along the lines of "What position are you in the market?" to whi...
Subsampling for dummies
A little piece of code dealing with the subsampling of matrices, in R. Useful if you want to use something akin to bootstrap, or just check the size of your sample with regard to various statistics.
A von Mises variate…
Inspired from a mail that came along the previous random generation post the following question rised : How to draw random variates from the Von Mises distribution? First of all let’s check the pdf of the probability rule, it is , for . Ok, I admit that Bessels functions can be a bit frightening,...
Welsh test by permutations
WelshPerm <- function(response,variable,nperm=999,...){ base <- oneway.test(response~variable,...) base.p <- base$p.value base.W <- base$statistic count <- 1 # Permutation loop for(i in 1:nperm){ SAMPLE <- sample(response) welsh.perm<-oneway.test(SAMPLE~variable,...) welsh.perm.p<-welsh.perm$p.value welsh.perm.W<-welsh.perm$statistic if(abs(welsh.perm.W) >= abs(base.W)) {count <- count+1} } result=count/(nperm+1) return(result) }
Sorting a matrix/data.frame on a column
mat.sort <- function(mat,n) { mat),] <- mat return(mat) } a <- matrix(rnorm(100),ncol=10) mat.sort(a,1)
GenEstim : A simple genetic algorithm for parameters estimation
The GenEstim function presented here uses a very simple genetic algorithm to estimate parameters. The function returns the best estimated set of parameters ($estim), the AIC ($information) at each generation, and the cost of the best model ($bestcost) at each generation. Results of running the program with a logistic function : Logis = function(x,p)...
An algorithm to find local extrema in a vector
I spend some time looking for an algorithm to find local extrema in a vector (time series). The solution I used is to “walk” through the vector by step larger than 1, in order to retain only one value even when the values are very noisy (see the picture at the end of the...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).