Question

我正在构建bootstrap函数，它将估计每个自举数据集的逻辑回归并进行一些额外的计算。对于小样本，有时它可能会产生警告，例如

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

我想为每个bootstrap迭代指出这样的警告。这是我的代码：

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)

在每次回归估算之前，我都要通过

重置警告

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

然后通过计算warnings()名称的长度来检查是否有警告。如果我手动执行此操作，而不应用parLapply，则此方法有效。但是，如果我正在运行此函数，即使有警告，它也只生成带有零的向量indicator。

数据生成代码：

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)))

如何在parLapply函数中指出警告

0 个答案: