I'm tryin to use gbm for the first time (actually any kind of regression tree for the first time) on my data, which consists of 14 continuous dependent variables and a factor as response variable with 13 levels. I came to gbm via a very good description by Elith et al., who however used a modification of the basic gbm package that can't handle multinomial distributions. The help of gbm claims that it can handle this:
"distribution: either a character string specifying the name of the distribution to use or a list with a component name specifying the distribution and any additional param-eters needed. If not specified, gbm will try to guess: if the response has only 2 unique values, bernoulli is assumed; otherwise, if the response is a factor, multinomial is assumed; otherwise, if the response has class "Surv", coxph is assumed; otherwise, gaussian is assumed. Currently available options are "gaussian" (squared error), "laplace" (absolute loss), "tdist" (t-distribution loss), "bernoulli" (logistic regression for 0-1 out-comes), "huberized" (huberized hinge loss for 0-1 outcomes), "multinomial" (classification when there are more than 2 classes), "adaboost" (the AdaBoost exponential loss for 0-1 outcomes), "poisson" (count outcomes), "coxph" (right censored observations), "quantile", or "pairwise" (ranking measure using the LambdaMart algorithm)."
Nevertheless, it doesn't work, no matter, whether I specify "multinomial" or "let it guess". Anyone any idea what I am doing wrong? Or am I misunderstanding something completely - does a multinomial distribution of my data not mean, that my error loss function is also of multinomial distribution? It runs if I chose "gaussian", but I guess in that case something completely different is calculated? I'd appreciate any help! agnes
Are you using the newest version of gbm? I had a similar issue which was resolved after re-installing the gbm package.