R仿真编程效率

Question

我是一个相对较新的R程序员，并编写了一个脚本，该脚本采用一些统计结果，并最终将其与目标变量随机化的结果向量进行比较。结果向量包含n次模拟的统计结果。随着模拟次数的增加(我希望至少运行10,000次模拟)，运行时间比我希望的要长。我已经尝试过用我知道的方法来修改代码来提高性能，但我希望别人能帮助我优化它。代码的相关部分如下:

#CREATE DATA   
require(plyr)
Simulations <- 10001
Variation <- c("Control", "A", "B","C")
Trials <- c(727,724,723,720)
NonResponse <- c(692,669,679,682)
Response <- c(35,55,44,38)
ConfLevel <- .95
#PERFORM INITIAL CALCS  
NonResponse <- Trials-Response
Data <-data.frame(Variation, NonResponse, Response, Trials)
total <- ddply(Data,.(Variation),function(x){data.frame(value = rep(c(0,1),times =   c(x$NonResponse,x$Response)))})
total <- total[sample(1:nrow(total)), ]
colnames(total) <- c("Variation","Response")
#CREATE FUNCTION TO PERFORM SIMULATIONS   
targetshuffle <- function(x) 
{
  shuffle_target <- x[,"Response"] 
  shuffle_target <- data.frame(sample(shuffle_target)) 
  revised <- cbind(x[,"Variation"], shuffle_target) 
  colnames(revised) <- c("Variation","Yes")
  yes_variation <- data.frame(table(revised$Yes,revised$Variation))
  colnames(yes_variation) <- c("Yes","Variation","Shuffled_Response")
  Shuffled_Data <- subset(yes_variation, yes_variation$Yes==1)
  Shuffled_Data <- Shuffled_Data[match(Variation, Shuffled_Data$Variation),]
  yes_variation <- cbind(Data,Shuffled_Data)
  VectorPTest_All <- yes_variation[,c("Variation","NonResponse","Response","Trials","Shuffled_Response")]
  Control_Only <- yes_variation[yes_variation$Variation=="Control",]
  VectorPTest_Chall <- subset(yes_variation,!(Variation=="Control"))
  VectorPTest_Chall <- VectorPTest_Chall[,c("Variation","NonResponse","Response","Trials","Shuffled_Response")] 
  ControlResponse <- Control_Only$Response
  ControlResponseRevised <- Control_Only$Shuffled_Response
  ControlTotal <- Control_Only$Trials
  VariationCount <- length(VectorPTest_Chall$Variation)          
  VP <- data.frame(c(VectorPTest_Chall,rep(ControlResponse),rep(ControlResponseRevised),rep(ControlTotal)))
  names(VP) <- c("Variation","NonResponse","Response", "Trials", "ResponseShuffled", "ControlReponse", 
             "ControlResponseShuffled","ControlTotal")
  VP1 <<- data.frame(VP[,c(5,7,4,8)]) 
  VP2 <<- data.frame(VP[,c(3,6,4,8)]) 
  ptest <- apply(VP1, 1, function(column) prop.test(x=c(column[1], column[2]), 
                                               n=c(column[3], column[4]),  alternative="two.sided", 
                                               conf.level=ConfLevel,  correct=FALSE)$p.value)
  min_p_value <- min(ptest)
  return(min_p_value)
}
#CALL FUNCTION   
sim_result <- do.call(rbind, rlply(Simulations, targetshuffle(total)))

Answer 1

首先要看的是如何创建所有的数据帧。每次这样做时，都是在复制组成对象中的所有数据。如果维度是可预测的，您可以考虑在函数的开头创建空矩阵，并在运行时填充它们。