I have used ifelse in count variables to count the number of times in a simulation the values of a vector of logprice fall within mutually exclusive ranges. However, there is a double count in the result i.e. i am getting output indicating values falling in mutually exclusive ranges. Here is the code and result R script niter = 1e5 # number of iterations is 10^5 CountLoss = rep(0,niter) CountProf = rep (0,niter) set.seed(2009) # enables reproducibility of result if script run again" for (i in 1:niter) { r = rnorm(100,mean=.05/253, sd=.23/sqrt(253)) # generate 100 random normal numbers logPrice = log(1e6) + cumsum(r) #vector of 100 days log prices maxlogP = max(logPrice) # max price over next 100 days minlogP = min(logPrice) CountLoss[i] <- ifelse (minlogP < log(950000), 1, ifelse (maxlogP > log (1000000), 0, 1)) CountProf[i] <- ifelse (maxlogP < log (1100000),0,1) } sum(CountLoss) mean(CountLoss) # fraction of times out of niter that stock is sold for a loss in a 100 day period sum(CountProf) mean(CountProf) # fraction of times out of niter that stock is sold for a profit in a 100 day period Output sum(CountLoss) [1] 64246> mean(CountLoss) # fraction of times out of niter that stock is sold for a loss in a 100 day period[1] 0.64246> sum(CountProf)[1] 51857> mean(CountProf) # fraction of times out of niter that stock is sold for a profit in a 100 day period[1] 0.51857 CountLoss and CountProf should sum to less than the number of interations. When I troubleshoot by reducing the number of iterations and that size of the logprice, I can't reproduce the contradicion.
On 26/06/2013 11:40 AM, Neville O'Reilly wrote:> I have used ifelse in count variables to count the number of times in a simulation the values of a vector of logprice fall within mutually exclusive ranges. However, there is a double count in the result i.e. i am getting output indicating values falling in mutually exclusive ranges. Here is the code and result > R script > niter = 1e5 # number of iterations is 10^5 > CountLoss = rep(0,niter) > CountProf = rep (0,niter) > set.seed(2009) # enables reproducibility of result if script run again" > for (i in 1:niter) > { > r = rnorm(100,mean=.05/253, > sd=.23/sqrt(253)) # generate 100 random normal numbers > logPrice = log(1e6) + cumsum(r) #vector of 100 days log prices > maxlogP = max(logPrice) # max price over next 100 days > minlogP = min(logPrice) > CountLoss[i] <- ifelse (minlogP < log(950000), 1, ifelse (maxlogP > log (1000000), 0, 1)) > CountProf[i] <- ifelse (maxlogP < log (1100000),0,1) > } > sum(CountLoss) > mean(CountLoss) # fraction of times out of niter that stock is sold for a loss in a 100 day period > sum(CountProf) > mean(CountProf) # fraction of times out of niter that stock is sold for a profit in a 100 day period > > Output > sum(CountLoss) > [1] 64246 > > mean(CountLoss) # fraction of times out of niter that stock is sold for a loss in a 100 day period > [1] 0.64246 > > sum(CountProf) > [1] 51857 > > mean(CountProf) # fraction of times out of niter that stock is sold for a profit in a 100 day period > [1] 0.51857 > > CountLoss and CountProf should sum to less than the number of interations. When I troubleshoot by reducing the number of iterations and that size of the logprice, I can't reproduce the contradicion.I don't see a contradiction. Both CountLoss and CountProf are less than niter. The logic of your test doesn't imply that sum(CountLoss) + sum(CountProf) should be less than niter; e.g. a case where minlogP is less than log(950000) and maxlogP > log(1100000) would be counted in both. Duncan Murdoch
On Jun 26, 2013, at 17:40 , Neville O'Reilly wrote:> I have used ifelse in count variables to count the number of times in a simulation the values of a vector of logprice fall within mutually exclusive ranges. However, there is a double count in the result i.e. i am getting output indicating values falling in mutually exclusive ranges. Here is the code and result > R scriptDon't use ifelse, it just confuses the logic. As far as I can tell, the code is equivalent (except for integer conversion) to CountLoss[i] <- (minlogP < log(950000)) | (maxlogP <= log (1000000)) CountProf[i] <- (maxlogP >= log (1100000)) and that doesn't look mutually exclusive to me.> niter = 1e5 # number of iterations is 10^5 > CountLoss = rep(0,niter) > CountProf = rep (0,niter) > set.seed(2009) # enables reproducibility of result if script run again" > for (i in 1:niter) > { > r = rnorm(100,mean=.05/253, > sd=.23/sqrt(253)) # generate 100 random normal numbers > logPrice = log(1e6) + cumsum(r) #vector of 100 days log prices > maxlogP = max(logPrice) # max price over next 100 days > minlogP = min(logPrice) > CountLoss[i] <- ifelse (minlogP < log(950000), 1, ifelse (maxlogP > log (1000000), 0, 1)) > CountProf[i] <- ifelse (maxlogP < log (1100000),0,1) > } > sum(CountLoss) > mean(CountLoss) # fraction of times out of niter that stock is sold for a loss in a 100 day period > sum(CountProf) > mean(CountProf) # fraction of times out of niter that stock is sold for a profit in a 100 day period > > Output > sum(CountLoss) > [1] 64246 >> mean(CountLoss) # fraction of times out of niter that stock is sold for a loss in a 100 day period > [1] 0.64246 >> sum(CountProf) > [1] 51857 >> mean(CountProf) # fraction of times out of niter that stock is sold for a profit in a 100 day period > [1] 0.51857 > > CountLoss and CountProf should sum to less than the number of interations. When I troubleshoot by reducing the number of iterations and that size of the logprice, I can't reproduce the contradicion. > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.-- Peter Dalgaard, Professor Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com
I don't see that you have set up mutually exclusive ranges. If we modify your code so save the maxlogP and minlogP values: niter = 1e5 # number of iterations is 10^5 CountLoss = rep(0,niter) CountProf = rep (0,niter) maxlogP <- rep(0, ninter) minlogP <- rep(0, ninter) set.seed(2009) # enables reproducibility of result if script run again" for (i in 1:niter) { r = rnorm(100,mean=.05/253, sd=.23/sqrt(253)) # generate 100 random normal numbers logPrice = log(1e6) + cumsum(r) #vector of 100 days log prices maxlogP[i] = max(logPrice) # max price over next 100 days minlogP[i] = min(logPrice) CountLoss[i] <- ifelse (minlogP[i] < log(950000), 1, ifelse (maxlogP[i] > log (1000000), 0, 1)) CountProf[i] <- ifelse (maxlogP[i] < log (1100000),0,1) } both <- which(CountLoss+CountProf>1) length(both) # 18484 head(both) # [1] 1 5 9 12 15 25 ifelse (minlogP[1] < log(950000), 1, ifelse (maxlogP[1] > log (1000000), 0, 1)) # [1] 1 ifelse (maxlogP[1] < log(1100000),0,1) # [1] 1 exp(maxlogP[1]) # [1] 1204589 exp(minlogP[1]) # [1] 932747.5 Your first simulation meets both criteria along 18483 others! ------------------------------------- David L Carlson Associate Professor of Anthropology Texas A&M University College Station, TX 77840-4352 -----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Neville O'Reilly Sent: Wednesday, June 26, 2013 10:41 AM To: r-help at r-project.org Subject: [R] Ifelse leading to inconsistent result I have used ifelse in count variables to count the number of times in a simulation the values of a vector of logprice fall within mutually exclusive ranges. However, there is a double count in the result i.e. i am getting output indicating values falling in mutually exclusive ranges. Here is the code and result R script niter = 1e5 # number of iterations is 10^5 CountLoss = rep(0,niter) CountProf = rep (0,niter) set.seed(2009) # enables reproducibility of result if script run again" for (i in 1:niter) { r = rnorm(100,mean=.05/253, sd=.23/sqrt(253)) # generate 100 random normal numbers logPrice = log(1e6) + cumsum(r) #vector of 100 days log prices maxlogP = max(logPrice) # max price over next 100 days minlogP = min(logPrice) CountLoss[i] <- ifelse (minlogP < log(950000), 1, ifelse (maxlogP> log (1000000), 0, 1))CountProf[i] <- ifelse (maxlogP < log (1100000),0,1) } sum(CountLoss) mean(CountLoss) # fraction of times out of niter that stock is sold for a loss in a 100 day period sum(CountProf) mean(CountProf) # fraction of times out of niter that stock is sold for a profit in a 100 day period Output sum(CountLoss) [1] 64246> mean(CountLoss) # fraction of times out of niter that stock issold for a loss in a 100 day period [1] 0.64246> sum(CountProf)[1] 51857> mean(CountProf) # fraction of times out of niter that stock issold for a profit in a 100 day period [1] 0.51857 CountLoss and CountProf should sum to less than the number of interations. When I troubleshoot by reducing the number of iterations and that size of the logprice, I can't reproduce the contradicion. ______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.