CODEWITHSUNDEEP
r
S31
S31

Reputation: 934

Looping Error - Object Not Found in R

Need help! Keep getting this error: 

Error: object 'freqSim' not found

From this portion of the code: 

indfreqDF <- function(x){
  frame <- severity[severity$industryType == x,]
  weightPercent <- frame$relfreq

  z <- largeAvgFunc2(freqDF)*weightPercent
  z <- z[-c(8)]
  # run a for loop to simulate 0/1 dataframe for each loss type

  for (i in 1:7){
    freqSim[i] <<- data.frame(sample(0:1, length(1:10000), replace=T, prob = c(1-z[i],z[i])))
  }
  return(freqSim[i])
} 

 indfreqDF("Software")
   Error in freqSim[i] <<- data.frame(sample(0:1, length(1:10000), replace = T,  : 
   object 'freqSim' not found

Ideally, what I would like is a 10,000x7 (row by col) dataframe/matrix of the 7 different unique damage types (damageType column, found within code below), with 10,000 randomly selected 0/1 values.

Code to Recreate: 

dec_amt <- function(x, k) format(round(x, k), nsmall=k)
severity <- data.frame(industryType = c("Consumer Products",
                                    "Biotech/Pharma",
                                    "Industrial/Construction",
                                    "Computer Hardware/Electronics",
                                     "Medical Devices",
                                     "Software",
                                    "Business/Consumer Services",
                                    "Telecommunications",
                                    "Automotive/Transportation",
                                      "Chemicals/Synthetic Materials",
                                      "All Industries"),
                                     relfreq = c(2.032520,
                                                 0.650407,
                                                 1.327913,
                                                 1.571816,
                                                 1.463415,
                                                 0.758808,
                                                 0.623306,
                                                 0.650407,
                                                 0.460705,
                                                 0.460705,
                                                 1.000000),
                                      relsev = c(0.419048,
                                                 3.771429,
                                                 0.609524,
                                                 2.019048,
                                                 3.028571,
                                                 1.314286,
                                                 0.723810,
                                                 4.247619,
                                                 0.152381,
                                                 0.076190,
                                                 1.000000))      

largeAvgFunc2 <- function(x) {
             three <- apply(x, 2, function(y) mean(tail(y,10)))
             four <- apply(x, 2, function(y) mean(tail(y,5)))

             avgMu2 <- apply(cbind(three,four), 1, mean)

             return(avgMu2)
             }


PIRATE <- data.frame(damageType = c("RR","RR","RR","LP","LP","LP","CD","CD","CD","ED","ED","ED","AF","AF","AF","CS","CS","CS","PI","PI","PI","TD","TD","TD")
                 ,Year = c(2000,2001,2002,2000,2001,2002,2000,2001,2002,2000,2001,2002,2000,2001,2002,2000,2001,2002,
                  2000,2001,2002,2000,2001,2002),
                 Count = c(4,4,9,18,17,20,15,15,12,12,12,12,15,12,12,53,1,3,5,6,7,2,12,6),
                 Amount = c(35222,12512012,12512,1251,1251251,12151,7771,1091,1091,121,121,194,1512,125125,125,621,
                            2631,136161,1321236,2136,111,3213,1262,6610))
PIRATE <- transform(PIRATE, medAmount = Amount/Count)
PIRATE <- transform(PIRATE, freqYearly = Count/c(18,
                                             16,
                                             45))

PIRATE$freqYearly <- dec_amt(PIRATE$freqYearly, 4);PIRATE$freqYearly <- as.numeric(PIRATE$freqYearly)
freqDF <- data.frame(PIRATE$damageType, PIRATE$freqYearly, PIRATE$Year)
freqDF <- acast(freqDF, PIRATE.Year ~ PIRATE.damageType, value.var = 'PIRATE.freqYearly')


indfreqDF <- function(x){
         frame <- severity[severity$industryType == x,]
         weightPercent <- frame$relfreq

         z <- largeAvgFunc2(freqDF)*weightPercent
         z <- z[-c(8)]

         # run a for loop to simulate 0/1 dataframe for each loss type

          for (i in 1:7){
          freqSim[i] <<- data.frame(sample(0:1, length(1:10000), replace=T, prob = c(1-z[i],z[i])))
         }
         return(freqSim[i])
         }                                        

indfreqDF("Software")

Upvotes: 0

Views: 1410

Answers (1)

johnson-shuffle
johnson-shuffle

Reputation: 1023

One possible way to get there would be to change indfreqDF to:

indfreqDF <- function(x){
  frame <- severity[severity$industryType == "Software",]
  weightPercent <- frame$relfreq

  z <- largeAvgFunc2(freqDF)*weightPercent
  z <- z[-c(8)]

  # run a for loop to simulate 0/1 dataframe for each loss type

  freqSim <- list()
  for (i in 1:7){
    freqSim[[i]] <- data.frame(sample(0:1, length(1:10000), replace=T, prob = c(1-z[i],z[i])))
  }

  do.call(cbind, freqSim)

}

Upvotes: 1

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