将 function 应用于值向量而不是 R 中的单个值

Question

我写了一个function来计算PAFPM2.5

这个 function 工作正常。

但是，在我尝试将 function 应用于一组矢量值的最后一步中出现致命错误。 在代码的第一行，我手动输入了值，代码在那里工作正常。 但我不需要 1 个值，但我需要 PAFPM2.5 来获取数百个值，从向量中读取 RR.low、RR.up 和 RR.est，如您在代码的第二行中所见。

不幸的是，这会导致错误：“PAF.summary 错误（low = sectors$RR.low，up = sectors$RR.up，est = sectors$RR.est）：未使用 arguments（low = sectors$RR.low , up = sectors$RR.up, est = sectors$RR.est)”

为清楚起见，sectors$RR.low 包含大约 100 个 RR.low 值，其他列也是如此。

PAFPM2.5<-PAF.summary(RR.low = 1.4, RR.up = 1.6, RR.est=1.5) #PM2.5

PAFPM2.5<-PAF.summary(low = sectors$RR.low, up = sectors$RR.up, est=sectors$RR.est) #PM2.5

##full script
library(readxl)
library(triangle)

sectors  <- read_excel("sectors.xlsx")  ##of handmatig inlezen via de knop (import dataset)
RR.low <- numeric()
RR.up <- numeric()
RR.est <- numeric()

RR.low <- sectors$RR.low
RR.up <- sectors$RR.up
RR.est <- sectors$RR.est


PAF.summary<-function(RR.low,RR.up,RR.est){
  
  ### RR.est is the point estimate of the RR
  ### (RR.low,RR.up) is the CI of the RR
  
  # relative risk
  r <- rtriangle(10000,a=RR.low,b=RR.up,c=RR.est)
  
  # proportion of population with risk factor
  p<-1.0  #in a statistical sector, 100% exposed to mean concentration within sector 
  
  # traditional PAF method
  PAF<-(p*(r-1))/(p*(r-1)+p)
  
  return(quantile(PAF,c(0.025,0.5,0.975)))
  
}


PAFPM2.5<-PAF.summary(RR.low = 1.4, RR.up = 1.6, RR.est=1.5)  #PM2.5  ###works fine

##should be converted to store many values based on the sectors file 
PAFPM2.5<-PAF.summary(RR.low = sectors$RR.low, RR.up = sectors$RR.up, RR.est=sectors$RR.est)  #PM2.5

Answer 1

一种方法是矢量化 function：

PAF.summary<-function(RR.low,RR.up,RR.est,id=NA){
  
  ### RR.est is the point estimate of the RR
  ### (RR.low,RR.up) is the CI of the RR
  
  library(triangle)
  
  # relative risk
  rtriangle2 <- Vectorize(rtriangle)
  r <- rtriangle2(10000,a=RR.low,b=RR.up,c=RR.est)
  
  # proportion of population with risk factor
  p<-1.0  #in a statistical sector, 100% exposed to mean concentration within sector 
  
  # traditional PAF method
  PAF<-(p*(r-1))/(p*(r-1)+p)
  output <- as.data.frame(t(apply(PAF,2,quantile,c(0.025,0.5,0.975))))
  if(!all(is.na(id))){
    output <- cbind(id,output)
  }
  return(output)
  

例子：

RR.low <- c(1.26,1.19)
RR.est <- c(1.34,1.25)
RR.up <- c(1.42,1.38)
SECTOR <- c("A575","A576")

PAF.summary(RR.low = 1.4, RR.up = 1.6, RR.est=1.5)
       2.5%       50%     97.5%
1 0.2970301 0.3332704 0.3665127


PAF.summary(RR.low, RR.up, RR.est,SECTOR)
    id      2.5%       50%     97.5%
1 A575 0.2175932 0.2538054 0.2865436
2 A576 0.1712424 0.2119270 0.2623992