How to indicate warnings inside of parLapply function

Question

I am constructing bootstrap function that will estimate logistic regression on each bootstrapped data set and do some additional calculations. For small samples sometimes it may produce warnings such as

Warning messages:
1: glm.fit: algorithm did not converge 
2: glm.fit: fitted probabilities numerically 0 or 1 occurred

I would like to indicate such warnings for each bootstrap iteration. Here is my code:

library(parallel)

n=length(y);
dataN=as.matrix((cbind(y,data)));
cl <- makeCluster(detectCores())
clusterExport(cl,c("dataN"), envir=environment())
clusterExport(cl,c("n"), envir=environment())
clusterEvalQ(cl,library(glmnet))
q=2000;
repl1=parLapply(cl=cl,1:q,function(i,dataA=dataN,smpl=n,...){
  dataB <- dataA[sample(nrow(dataA),size=smpl,replace=TRUE),]
  p=dim(dataB)[2];p
  forname=c(colnames(dataB)[2:p]);
  formulaZS=as.formula(paste("y~",paste(forname,collapse="+"),sep=""));
  assign("last.warning", NULL, envir = baseenv())
  m=glm(formulaZS,family=binomial,data=data.frame(dataB))
  coefs=summary(m)$coef[,1];coefs
  a=length(attr(warnings(),"names"))
  indicator=ifelse(a==0,0,1);
  return(list(coefs=coefs,indicator=indicator))
})  
stopCluster(cl)

beta=t(matrix(unlist(lapply(repl1, "[[", "coefs")),nrow=p+1));
beta.est=apply(beta,2,mean);beta.est
indicator=t(matrix(unlist(lapply(repl1, "[[", "indicator")),nrow=1));
sum(indicator)

Before each regression estimation I'm resetting warnings by

assign("last.warning", NULL, envir = baseenv())

and then checking if there was warning by calculating the length of warnings() names. This works if I'm doing it manually, without application of parLapply. However, if I'm running this function it produces the vector indicator with only zeroes even if there were warnings.

Data generating code:

fundata=function(n,p,corr){
      R = matrix(rep(corr,p*p),nrow=p)+(1-corr)*diag(p);
      R <- round(((R * lower.tri(R)) + t(R * lower.tri(R))),2)
      diag(R) <- 1 
      U = t(chol(R))
      nvars = dim(U)[1]
      random.normal = matrix(rnorm(nvars*n,mean=0,sd=1), nrow=nvars, ncol=n);
      X = U %*% random.normal
      newX = t(X)
      data = as.data.frame(newX)
      names(data)<-sprintf("V%d",1:p)
      return(data=data) 
}
n=50;
p=5;
corr=0.5;
seed=123;
#Generate independent variables
data=fundata(n,p,corr);
#Generate dependent variable
p=dim(data)[2];p
  true=c(-1,0,0,0.2,0.675,-1.5)
  z=as.matrix(cbind(rep(1,n),data[,1:p]))%*%true;
  pr = 1/(1+exp(-z));
  y = rbinom(n=n, size=1, prob=t(pr))
  dataN=as.matrix((cbind(y,data)))