(R) function: object not found: environment depth fine? - r

Question

I'm puzzled by a function error & would appreciate some insight.
The function, very briefly, automates the multiple processes involved in Boosted Regression Trees using gbm.step & other gbm's.
"gbm.auto" <- function (grids, samples, 3 parameters) {
starts 2 counters, require(gbm), does various small processing jobs with grids & samples
for parameter 1{
for parameter 2{
for parameter 3{
Runs 2 BRTs per parameter-combination loop, generates & iteratively updates a 'best' BRT for each, adds to counters. Extensive use of samples.
}}}
closes the loops, function continues as the first } is still open.
The next BRT can't find samples, even though it's at the same environment depth (1?) as the pre-loop processing jobs which used it successfully. Furthermore, adding "globalsamples<<-samples" after the }}} loop successfully saves the object, suggesting that samples is still available. Adding env1,2 & 3<<-environment() before the {{{ loop, within it & after it results in Environment for all three. Also suggesting it's all the same function environment & samples should be available.
What am I missing here? Thanks in advance!
Edit: exact message:
Error in eval(expr, envir, enclos) : object 'samples' not found
Function - loads removed & compacted but still gives same error message:
"gbm.auto" <-
function (samples, expvar, resvar, tc, lr, bf)
{ # open function
require(gbm)
require(dismo)
# create binary (0/1) response variable, for bernoulli BRTs
samples$brv <- ifelse(samples[resvar] > 0, 1, 0)
brvcol <- which(colnames(samples)=="brv") # brv column number for BRT
for(j in tc){ # permutations of tree complexity
for(k in lr){ # permutations of learning rate
for(l in bf){ # permutations of bag fraction
Bin_Best_Model<- gbm.step(data=samples,gbm.x = expvar, gbm.y = brvcol, family = "bernoulli", tree.complexity = j, learning.rate = k, bag.fraction = l)
}}} # close loops, producing all BRT/GBM objects & continue through model selection
Bin_Best_Simp_Check <- gbm.simplify(Bin_Best_Model) # simplify model
# if best number of variables to remove isn't 0 (i.e. it's worth simplifying), re-run the best model (Bin_Best_Model, using gbm.call to get its values)
# with just-calculated best number of variables to remove, removed. gbm.x asks which number of drops has the minimum mean (lowest point on the line)
# & that calls up the list of predictor variables with those removed, from $pred.list
if(min(Bin_Best_Simp_Check$deviance.summary$mean) < 0)
assign("Bin_Best_Simp", gbm.step(data = samples,
gbm.x = Bin_Best_Simp_Check$pred.list[[which.min(Bin_Best_Simp_Check$deviance.summary$mean)]],
gbm.y = brvcol, family = "bernoulli", tree.complexity = j, learning.rate = k, bag.fraction = l))
}
Read in data:
mysamples<-data.frame(response=round(sqrt(rnorm(5000, mean= 2.5, sd=1.5)^2)),
depth=sqrt(rnorm(5000, mean= 35, sd=24)^2),
temp=rnorm(5000, mean= 15, sd=1.2),
sal=rnorm(5000, mean= 34, sd=0.34))
Run this: gbm.auto(expvar=c(2,3,4),resvar=1,samples=mysamples,tc=2,lr=0.00000000000000001,bf=0.5)
Problem now: this causes a different error because my fake data are somehow wrong. ARGHG!
Edit: rounded the response data to integers and kept shrinking the learning rate until it runs. If it doesn't work for you, add zeroes until it does.
Edit: so this worked on my computer but reading it back to a clean sheet from online fails on a DIFFERENT count:
Error in var(cv.cor.stats, use = "complete.obs") :
no complete element pairs
In cor(y_i, u_i) : the standard deviation is zero
Is it allowed to attach or link to a csv of a small clip of my data? I'm currently burrowing deeper & deeper into bugfixing problems created by using fake data which I'm only using for this question, & thus getting off topic from the actual problem. Exasperation mode on!
Cheers
Edit2: if this is allowed: 1000row 4column csv link here: https://drive.google.com/file/d/0B6LsdZetdypkaC1WYXpKU3ZScjQ