[Biomod-commits] problems with RF and warning messages

[Daisy Englert Duursma]

Hello,

I am having some problems running the model with my presence and
absence data.  I am running the model for a single species and have
used  a weight matrix so the sum of my observations (944 observations)
is equal to the absences (38181 absences).  I am having  2 problems:

1)	When I run the models everything goes smoothly until the model RF.
Then I get the error message: Model=Breiman and Cutler's random
forests for classification and regression  Error: cannot allocate
vector of size 447.8 Mb.  I understand this is a common problem but I
only receive this error with the RF model. If I turn off all the other
models it still does not run. Additionally, the RF does run when I use
pseudo-absences instead of weighting the absences in my data. I have
also made sure to: rm(list = ls()), gc(), memory.limit(size = 4095).
Any advice would be helpful.

The following is how I have set up my model:
Models(GLM = T, TypeGLM = "poly", Test = "AIC", GBM = T, No.trees =
3000, GAM = T, Spline = 3, CTA = T, CV.tree = 50, ANN = T, CV.ann = 2,
SRE = T, Perc025=T, Perc05=F, MDA = T, MARS = T, RF = T, NbRunEval =
3, DataSplit = 80, Yweights=weightsall$COPORNA, Roc = T,
Optimized.Threshold.Roc = T,Kappa = T, TSS=T, KeepPredIndependent = T,
VarImport=5,  NbRepPA=0, strategy="circles", coor= NULL, distance= 0 ,
nb.absences=NULL)

2)	After running this model I also receive an abundance of warning
messages including:
In gbm.perf(model.sp, method = "OOB", plot.it = F) :   OOB generally
underestimates the optimal number of iterations although predictive
performance is reasonably competitive. Using cv.folds>0 when calling
gbm usually results in improved predictive performance.
In glm.fit(X, y, wt, offset = object$offset, family = object$family,
... :   algorithm did not converge
In glm.fit(X, y, wt, offset = object$offset, family = object$family,
... :   fitted probabilities numerically 0 or 1 occurred

Do I need to worry about these?

Thank you,

Daisy Englert
Macquarie University
Sydney, NSW