search for: haitian

...lassifiers with extremely high AIC (over 200), no perfect separation, coefficients converge. in this case, using brglm() does help! It stabilize the AIC, and the classification power is better. Code and output: (need to install package: brglm) matrix <- read.table("http://ihome.ust.hk/~haitian/sample.txt") names(matrix)<- c("g0","g761","g2809","g3106","g4373","g4583") fo <- as.formula(g0 ~ g761 * g2809 * g3106 * g4373 * g4583) library(MASS) library(brglm) lr <- brglm(formula= fo, family=binomial(link=logit), data=...

Hello List, I'm trying to help a family from the Dominican Republic and to do so need a temporary DID from DR. The short story is that there is a 2 year old here with a serious heart defect from a remote area of Dominican Republic near the Haitian border. He was referred to Gift of Life D.R. who eventually contacted Gift of Life Central Florida and this is where I'm involved. The child and mother are here in the US and the child had surgery last week. I need a DR DID to help facilitate phone calls home to the father in DR who couldn...

Dear R-users, I use glm() to do logistic regression and use stepAIC() to do stepwise model selection. The common AIC value comes out is about 100, a good fit is as low as around 70. But for some model, the AIC went to extreme values like 1000. When I check the P-values, All the independent variables (about 30 of them) included in the equation are very significant, which is impossible, because we

Hi, I run the following tuning function for svm. It's very strange that every time i run this function, the best.parameters give different values. [A] >svm.tune <- tune(svm, train.x, train.y, validation.x=train.x, validation.y=train.y, ranges = list(gamma = 2^(-1:2), cost = 2^(-3:2))) # where train.x and train.y are matrix