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我正在模拟数据并比较二元 logit 模型的 glm.fit、bigglm、speedglm、glmnet、LiblineaR。

testGLMResults_and_speed <- function(N, p, chunk=NULL, Ifsample=FALSE, size=NULL, reps=5){

  library(LiblineaR)
  library(speedglm)
  library(biglm)
  library(glmnet)

  # simulate dataset
  X = scale(matrix(rnorm(N*p), nrow=N, ncol=p))
  X1 = cbind(rep(1, N), X)
  q = as.integer(p/2)
  b = c(rnorm(q+1), rnorm(p-q)*10)
  eta = X1 %*% b

  # simulate Y
  simy <- function(x){
    p = 1/(1 + exp(-eta[x]))
    u = runif(1, 0, 1)
    return(ifelse(u<=p, 1, 0))
  }

  Y = sapply(1:N, simy)
  XYData = as.data.frame(cbind(y=Y, X))

  getSample <- function(X, Y=NULL, size){
    ix = sample(dim(X)[1], size, replace=FALSE)    
    return(list(X=X[ix,], Y=Y[ix]))
  }

  #LiblineaR function
  fL <- function(X, Y, type, Ifsample=Ifsample, size=size){    
    if(Ifsample){
      res = getSample(X, Y, size)
      X = res$X; Y = res$Y;
    }
    resL = LiblineaR(data=X, labels=Y, type=type)
    return(resL$W)    
  }

  #glmnet
  fNet <- function(X, Y, Ifsample=Ifsample, size=size){    
    if(Ifsample){
      res = getSample(X, Y, size)
      X = res$X; Y = res$Y;
    }
    resNGLM <- glmnet(x=X, y=Y, family="binomial", standardize=FALSE, type.logistic="modified.Newton")    
    return(c(resNGLM$beta[, resNGLM$dim[2]], 0))
  }

  #glm.fit
  fglmfit <- function(X1, Y, Ifsample=Ifsample, size=size){
    if(Ifsample){
      res = getSample(X1, Y, size)      
      X1 = res$X; Y=res$Y;
    }
    resGLM = glm.fit(x=X1, y=Y, family=binomial(link=logit))
    return(c(resGLM$coefficients[2:(p+1)], resGLM$coefficients[1]))
  }

  #speedglm
  fspeedglm <- function(X1, Y, Ifsample=Ifsample, size=size){
    if(Ifsample){
      res = getSample(X1, Y, size)  
      X1 = res$X; Y=res$Y;
    }
    resSGLM = speedglm.wfit(y=Y, X=X1, family=binomial(link=logit), row.chunk=chunk)
    return(c(resSGLM$coefficients[2:(p+1)], resSGLM$coefficients[1]))
  }

  #bigglm
  fbigglm <- function(form, XYdf, Ifsample=Ifsample, size=size){
    if(Ifsample){
      res = getSample(XYdf, Y=NULL, size)  
      XYdf = res$X; 
    }
    resBGLM <- bigglm(formula=form, data=XYdf, family = binomial(link=logit), maxit=20)
    if(resBGLM$converged){
      resBGLM = summary(resBGLM)$mat[,1]
    } else {
      resBGLM = rep(-99, p+1)
    }    
    return(c(resBGLM[2:(p+1)], resBGLM[1]))
  }

  ## benchmarking function
  ## calls reps times and averages parameter values and times over reps runs
  bench_mark <- function(func, args, reps){
    oneRun <- function(x, func, args){
      times = system.time(res <- do.call(func, args))[c("user.self", "sys.self", "elapsed")]
      return(list(times=times, res=res))
    }    
    out = lapply(1:reps, oneRun, func, args)
    out.times = colMeans(t(sapply(1:reps, function(x) return(out[[x]]$times))))
    out.betas = colMeans(t(sapply(1:reps, function(x) return(out[[x]]$res))))
    return(list(times=out.times, betas=out.betas))
  }

  #benchmark LiblineaR
  func="fL"
  args = list(X=X, Y=Y, type=0, Ifsample=Ifsample, size=size)
  res_L0 = bench_mark(func, args, reps)
  args = list(X=X, Y=Y, type=6, Ifsample=Ifsample, size=size)
  res_L6 = bench_mark(func, args, reps)

  #benchmark glmnet 
  func = "fNet"
  args = list(X=X, Y=Y, Ifsample=Ifsample, size=size)
  res_GLMNet = bench_mark(func, args, reps)

  func="fglmfit"
  args = list(X1=X1, Y=Y, Ifsample=Ifsample, size=size)
  res_GLM = bench_mark(func, args, reps)

  func="fspeedglm"
  args = list(X1=X1, Y=Y, Ifsample=Ifsample, size=size)
  res_SGLM = bench_mark(func, args, reps)  

  func = "fbigglm"
  # create formula for bigglm
  xvarname = paste("V", 2:dim(XYData)[2], sep="")
  f  = as.formula(paste("y ~ ", paste(xvarname, collapse="+")))
  args = list(form=f, XYdf=XYData, Ifsample=Ifsample, size=size)
  res_BIGGLM = bench_mark(func, args, reps)

  summarize <- function(var){
    return(rbind(L0=res_L0[[var]], L6=res_L6[[var]], 
                GLMNet=res_GLMNet[[var]], GLMfit=res_GLM[[var]], 
                speedGLM=res_SGLM[[var]], bigGLM=res_BIGGLM[[var]]))
  }

  times = summarize("times")
  betas = rbind(summarize("betas"), betaTRUE = c(b[2:(p+1)], b[1]))
  colnames(betas)[dim(betas)[2]] = "Bias"
  # compare betas with true beta
  betacompare = t(sapply(1:dim(betas)[1], function(x) betas[x,]/betas[dim(betas)[1],]))


  print(paste("Run times averaged over", reps, "runs"))
  print(times)

  print(paste("Beta estimates averaged over", reps, "runs"))
  print(betas)

  print(paste("Ratio Beta estimates averaged over", reps, "runs for all methods (reference is true beta)"))
  print(betacompare)
}

示例运行如下所示：

> testGLMResults_and_speed(10000, 5, 500, FALSE, 5000, 5)
[1] "Run times averaged over 5 runs"
         user.self sys.self elapsed
L0          0.0152   0.0032  0.0106
L6          0.0108   0.0002  0.0108
GLMNet      0.5660   0.0002  0.5666
GLMfit      0.0836   0.0262  0.0576
speedGLM    0.0438   0.0122  0.0298
bigGLM      0.2414   0.0956  0.1822
[1] "Beta estimates averaged over 5 runs"
                 V1         V2        V3        V4        V5       Bias
L0        0.4611973  0.7113034 -7.373351  4.890485  2.699251  0.3026018
L6        0.4516369  0.7002171 -7.284820  4.829202  2.659993  0.2972566
GLMNet   -0.4873854 -0.7512806  7.839316 -5.200533 -2.868255  0.0000000
GLMfit   -0.5064564 -0.7773168  8.088214 -5.367488 -2.961743 -0.3326801
speedGLM -0.5064564 -0.7773168  8.088214 -5.367488 -2.961743 -0.3326801
bigGLM   -0.5064564 -0.7773168  8.088214 -5.367488 -2.961743 -0.3326801
betaTRUE -0.4377651 -0.7692994  8.290677 -5.442880 -3.088798 -0.3901147
[1] "Ratio Beta estimates averaged over 5 runs for all methods (reference is true beta)"
            V1         V2         V3         V4         V5       Bias
[1,] -1.053527 -0.9246119 -0.8893544 -0.8985106 -0.8738838 -0.7756739
[2,] -1.031688 -0.9102009 -0.8786760 -0.8872513 -0.8611741 -0.7619724
[3,]  1.113349  0.9765776  0.9455579  0.9554745  0.9285990  0.0000000
[4,]  1.156914  1.0104217  0.9755794  0.9861486  0.9588659  0.8527751
[5,]  1.156914  1.0104217  0.9755794  0.9861486  0.9588659  0.8527751
[6,]  1.156914  1.0104217  0.9755794  0.9861486  0.9588659  0.8527751
[7,]  1.000000  1.0000000  1.0000000  1.0000000  1.0000000  1.0000000
Warning messages:
1: glm.fit: fitted probabilities numerically 0 or 1 occurred 
2: glm.fit: fitted probabilities numerically 0 or 1 occurred 
3: glm.fit: fitted probabilities numerically 0 or 1 occurred 
4: glm.fit: fitted probabilities numerically 0 or 1 occurred 
5: glm.fit: fitted probabilities numerically 0 or 1 occurred

我经常看到来自 LiblineaR 的估计值很接近，但迹象却相反！有人知道为什么会这样吗？它通常是最快的，并且在我见过的更大数据集的情况下甚至更快。

我知道由于正则化，这些值不会相同；我对这些迹象更好奇。

如果有人（rep>1500）可以请添加#LiblineaR 和#speedglm 标签，我将不胜感激。

4

1 回答 1

1

虽然我没有使用LiblineaR，但当逻辑回归估计与您期望的相反标签的概率时，就会发生这种情况。其他一切都在估计 Y = 1 的概率，所以我的猜测是LiblineaR预测 Y = 0 的概率。

于 2013-10-23T12:35:06.853 回答