search for: stepclass

Hi! I got the following runtime error when I tried to use svm method with stepclass. Error in "colnames<-"(`*tmp*`, value = c("0", "1")) : attempt to set colnames on object with less than two dimensions I repeated the same sequence of statements but this time I used the classification function used in the example, i.e., "lda" an...

Hi, I'm trying to use "stepclass" to do a stepwise variable selection with method=lda. I keep getting this warning message, which shows up once for each variable added to the model during variable selection: Warning message: error(s) in modeling/prediction step in: cv.rate(vars = c(model, tryvar), data = data, grouping = gro...

Hi, a colleague ran a stepwise discriminant analysis twice in a row and got different results, suggesting some "sochasticity" in the algorithms involved. I looked at her data and found that there was a lot of collinearity, so that I reckoned that maybe "stepclass" (klaR) cannot find a clear winner when trying to include a new variable and makes a random choice. Is that true? another possibility is that "lda" (from MASS) computes CV classification rates from a random subsample instead of using all the data (?) That might be a sensible choice...

I use Windows, R version 2.5.1 When I try to run stepclass (klaR) I get an error message/warning saying: 1: error(s) in modeling/prediction step in: cv.rate(vars = c(model, tryvar), data = data, grouping = grouping, ... Actually, I look 16 warnings of this type. Can anyone tell me what this means? Also, it returns only 2 out of the 79 variables as im...

Dear R users, I have some environmental variables and I need to find the best combination of them in order to separate two main groups (coded 1 and 2). I have performed a discriminant analysis using the stepclass function as a method for selecting the most relevant environmental variables. The problem is that this function includes a parameter (start.vars) and my results change a lot when I change this variable...Oh my God!!! Then, one possible functionl is not the best for my data... grupo<-stepclass(...

...LDA) using "MASS::lda", with acceptable results. However, one of my collaborators suggested I use multinomial regression instead. I think his suggestion is mainly concerned with the choice of which variables (sets) best describe the data. I have used a stepwise approach (using klaR::stepclass) using the proportion of correct classifications to choose among the sets of variables. However I've been suggested that use a method that will give out an AIC instead, that will "penalize" the use of more variables. I have never done multinomial regression, and am uncertain about...