[R] MART(tm) vs. gbm

[manuel.martin]

Hello,
I have been using two different implementations of the stochastic 
gradient boosting (Friedman 2002) : MART(tm) with R and the gbm package. 
Both are fairly comparable except that the MART with R systematically 
strongly (depending on the dataset though) outperforms the gbm tool in 
terms of goodness of fit.
For instance, a

# gbm package
   gbm1 <- gbm(Y~X2+X3+X4+X5+X6,
          data=data,
          var.monotone=c(0,0,0,0,0),   #  0: no monotone restrictions
          distribution="gaussian",     # bernoulli, adaboost, gaussian,
                                       # poisson, and coxph available
          n.trees=3000,                # number of trees
          shrinkage=0.005,             # shrinkage or learning rate,
                                       # 0.001 to 0.1 usually work
          interaction.depth=6,         # 1: additive model, 2: two-way 
interactions, etc.
          bag.fraction = 0.5,          # subsampling fraction, 0.5 is 
probably best
          train.fraction = 0.5,        # fraction of data for training,
                                       # first train.fraction*N used for 
training
          n.minobsinnode = 10,         # minimum total weight needed in 
each node
          cv.folds = 5,                # do 5-fold cross-validation
          keep.data=TRUE,              # keep a copy of the dataset with 
the object
          verbose=TRUE)                # print out progress

# MART with R
X <- as.matrix(cbind(data$X2,as.numeric(data$X3), 
as.numeric(data$X4),as.numeric(data$X5),data$X6))
Y <- data$Y
mart(X, Y, c(1,2,2,2,1) , niter=3000, tree.size=6,    learn.rate=0.005, 
loss.cri=2 #gaussian too
        )

leads to very different goodnesses of fit (I can provide the dataset if 
needed).

Did anyone already encountered this, is there an explanation, am I 
missing something obvious in the argument settings?
Thank you in advance,

Manuel