search for: underfitting

Hi, I'm trying to use mars function in R to interpolate nonlinear multivariate functions. However, it seems that mars gives me a fit which uses only very few basis function and it underfits very badly. For example, I have tried the following code to test mars: require("mda") f <- function(x,y) { x^2-y^2 }; #f <- function(x,y) { x+2*y }; # Grid x <-

Hello, I have a question regarding model selection and dropping of terms for GAMs fitted with package mgcv. I am following the approach suggested in Wood (2001), Wood and Augustin (2002). I fitted a saturated model, and I find from the plots that for two of the covariates, 1. The confidence interval includes 0 almost everywhere 2. The degrees of freedom are NOT close to 1 3. The partial

Dear Users, In case of ridge logistic regression, i want to calculate the optimum penalty using aic and bic criteria. Here is the sample code: fit <- lrm(RES ~CAT01+NUM01+NUM02+CAT02+CAT03+CAT04+NUM03+CAT05+CAT06+NUM04+ CAT07+CAT08+NUM05+NUM06, data = train.data, x = TRUE, y = TRUE) pentrace(fit, penalty = list(seq(.001, 5, by=.1))) output: Best penalty: penalty df 1.001

I thought I would have another try at explaining my problem. I think that last time I may have buried it in irrelevant detail. This output should explain my dilemma: > dim(S) [1] 1455 269 > summary(as.vector(S)) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.160e+04 0.000e+00 0.000e+00 -4.132e-08 0.000e+00 8.636e+03 > sum(as.vector(S)==0)/(1455*269) [1]