优化函数的性能

Question

我在下面创建了以下函数，用于在 data.frame 的新列中将真实值与预测值（当真实值不存在时）合并，该函数确实有效，但我想对其进行优化，因为使用数据集我工作，该功能大约需要两个小时才能运行..如果有人可以帮助我，我将不胜感激。

p <-            
  function(object, newdata = NULL, type = c("link", "response", "terms"), 
           rse.fit = FALSE, dispersion = NULL, terms = NULL,
           na.action = na.pass, ...)
  { 
{
    pred <- predict (object,newdata)    

      }

    vetor1 <- (newdata$ALT)         # Creates a column vector from the actual heights of the data.frame
    vetor1[is.na(vetor1)] <- 0      # Replaces the NA's present in the vector created above the numeric value 0
    vetor2 <- c(pred)           # Creates a vector from the predicted data
    for(i in 1:length(vetor1)){     # The loop is executed until all values vector1 pass the following condition
      if(vetor1[i]==0.00){      # If a value of the first vector has the value 0, ie, if it is absent
        vetor1[i]=vetor2[i]     # Then the predicted value will replace the missing value
        newdata$ALTMISTA <- vetor1  # The vector1, already possessing the actual values and the predicted values merged into the same vector goes                   on to become a new column in data.frame, this column is called a ALTMISTA
      }
    }
    return (newdata)            
  }

Answer 1

几个想法：如果你有一个 gigundo 数据集，那么这需要时间；或者你需要学习使用parallel 包。

我认为您不想每次通过循环都重新定义newdata$ALTMISTA，因为您只是在覆盖这些值。

您可以通过对ifelse 使用矢量化操作来删除i 循环：

 set.seed(1)
 foo<-sample(c(-1,1),10,rep=T)
 foo
 [1] -1 -1  1  1 -1  1  1  1  1 -1
 bar<-11:20
 foo<- ifelse(foo<0, foo,bar)
 foo
 [1] -1 -1 13 14 -1 16 17 18 19 -1

但正如我所说，我怀疑你有一个大型数据集，predict 很可能是时间猪。尝试使用Rprof 找出时间都花在了哪里。