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Full_Name: Jeff Schwarz Version: R1.3.1 OS: Windows 2000 Submission from: (NULL) (129.22.170.115) Error message (using predict and predict.nnet) > predict (smalltest, smallx[-jj,]) Error in matrix(NA, length(keep), nout, dimnames = list(rn, dimnames(object$fitted)[[2]])) : length of dimnames[1] not equal to array extent *** all relevant code and data source is given below *** I have successfully built an object of type nnet using... nnet.formula(formula = Q6...

...anyway to make it run faster? 2. I noticed a difference in RAM consumption when specifying loess function differently. The first case is I ran loess(z ~ x+y,data=X,degree=1,alpha=.5) where my X has about 50 columns. The second case I trimmed X to only contain x,y and z. Then run loess(z ~ x+y, data=SmallX, degree=1, alpha=.5). I find that the second case consumes only 4% of RAM whereas the first case uses up to 33%. I'd like to know how loess handles input data under the hood? It seems to me it attaches the whole data into memory and hence results in what I observed. Is my understanding correct?...

Dear Users, I have one problem here, I tried many time and even read a few notes on writing function but still..... Can anyone help me on how to simplify Part B (please refer the programming below), so that I don't have to repeat the calculation of "num" and "r" ? Thank you very much..every help is very much appreciated... ## Part A n=1400 m=matrix(c(0,0,0,0),4,1)