Dear Sir,
Yes I am using the plyr and in the end I am writing the output to the
data.frame. Earlier I had the problem of process time and hence I made some
changes in the code and now I am fetching all the required inputs needed for
valuation purpose using ddply, store the results in a data.frame and once that
is over, I am carrying out the calculations.
Here is part of my R code-
library(plyr)
library(reshape)
tx <- read.csv('transaction_fxdeal.csv')
tx$id <- as.character(tx$id)
n <- max(unique(simulated_exchange$id))
result <- NULL
current <- 1
rcount <- 0
current1 <- 1
rcount1 <- 0
current2 <- 1
rcount2 <- 0
for (env in 0:n) {
if (rcount == 0) rcount <- nrow(subset(simulated_interest, id==env))
temp <- current+rcount-1
env_rates <- simulated_interest[current:temp,]
env_rates <- env_rates[order(env_rates$curve, env_rates$day_count), ]
if (rcount1 == 0)rcount1 <- nrow(subset(simulated_exchange, id==env))
temp <- current1+rcount1-1
exch_rates <- simulated_exchange[current1:temp,]
if (rcount2 == 0)rcount2 <- nrow(subset(simulated_instruments, id==env))
temp <- current2+rcount2-1
instr_rates<- simulated_instruments[current2:temp,]
current <- current+rcount
current1 <- current1+rcount1
current2 <- current2+rcount2
curve <- daply(env_rates, 'curve', function(x) {
return(approxfun(x$day_count, x$rate, rule = 2))
})
result <- rbind(result, ddply(tx, 'id', function(x) {
intrate_from <- curve[[x$currency_from]](x$maturity_from)
intrate_to <- curve[[x$currency_to]](x$maturity_to)
cross_rate <- subset(exch_rates, key==paste(x$currency_from_exch,
x$currency_to_exch, sep='_'))$rate
base_rate <- subset(exch_rates, key==paste(x$currency_to_exch,
x$currency_base, sep='_'))$rate
return(data.frame(env=env, intrate_from=intrate_from, intrate_to=intrate_to,
cross_rate=cross_rate, base_rate=base_rate))
}))
}
sorted <- result[order(result$id, result$env),]
sorted$currency_from_exch <- rep(tx$currency_from_exch, each =
length(unique(sorted$env)))
sorted$currency_to_exch <- rep(tx$currency_to_exch, each =
length(unique(sorted$env)))
sorted$currency_base <- rep(tx$currency_base, each = length(unique(sorted$env)))
sorted$transaction_type <- rep(tx$transaction_type, each =
length(unique(sorted$env)))
sorted$amount_fromccy <- rep(tx$amount_fromccy, each =
length(unique(sorted$env)))
sorted$amount_toccy <- rep(tx$amount_toccy, each = length(unique(sorted$env)))
sorted$intbasis_fromccy <- rep(tx$intbasis_fromccy, each =
length(unique(sorted$env)))
sorted$intbasis_toccy <- rep(tx$intbasis_toccy, each =
length(unique(sorted$env)))
sorted$maturity_from <- rep(tx$maturity_from, each = length(unique(sorted$env)))
sorted$maturity_to <- rep(tx$maturity_to, each = length(unique(sorted$env)))
sorted$currency_from <- rep(tx$currency_from, each =
length(unique(sorted$env)))
sorted$currency_to <- rep(tx$currency_to, each = length(unique(sorted$env)))
sorted$from_mtm <- sorted$cross_rate * (sorted$amount_fromccy / ((1 +
(sorted$intrate_from/100))^(sorted$maturity_from / sorted$intbasis_fromccy)))
sorted$to_mtm <- (sorted$amount_toccy / ((1 +
(sorted$intrate_to/100))^(sorted$maturity_to / sorted$intbasis_toccy)))
mtm_base <- function(from_mtm, to_mtm, base_rate)
{
mtm <- (from_mtm + to_mtm)
mtm_bc = mtm*base_rate[1]
return(data.frame(mtm_bc = mtm_bc))
}
sorted1 <- ddply(.data=sorted, .variables = "id", .fun=function(x)
mtm_base(from_mtm = x$from_mtm, to_mtm = x$to_mtm, base_rate = x$base_rate))
sorted$mtm <- sorted1$mtm
sorted$mtm_bc <- sorted1$mtm_bc
sorted2 <- ddply(sorted, .(currency_from_exch, id), mutate, change_in_mtm_bc =
mtm_bc - mtm_bc[1])
sorted$change_in_mtm_bc <- sorted2$change_in_mtm_bc
sorted <- sorted[order(sorted$id, sorted$env),]
write.csv(data.frame(sorted), file='MC_result_fxdeal.csv', row.names=FALSE)
# ________________________________________________________
# END of Code
With regards
Amelia
On Wednesday, 6 April 2016 7:43 PM, Jeff Newmiller <[email protected]>
wrote:
As Jim has indicated, memory usage problems can require very specific
diagnostics and code changes, so generic help is tough to give.
However, in most cases I have found the dplyr package to be more memory
efficient than plyr, so you could consider that. Also, you can be explicit
about only saving the minimum results you want to keep rather than making a
list of complete results and extracting results later.
--
Sent from my phone. Please excuse my brevity.
On April 6, 2016 4:39:59 AM PDT, Amelia Marsh via R-help <[email protected]>
wrote:
Dear R Forum,
>
>I have about 2000+ FX forward transactions and I am trying to run 1000
>simulations. If I use less no of simulations, I am able to get the desired
>results. However, when I try to use more than 1000 simulations, I get
>following error.
>
>
>sorted2 <- ddply(sorted, .(currency_from_exch, id), mutate, change_in_mtm_bc =
>mtm_bc - mtm_bc[1])
>>
>Error: cannot allocate vector of size 15.6 Mb
>
>
>In addition: Warning messages:
>1: Reached total allocation of 3583Mb: see help(memory.size)
>2: Reached total allocation of 3583Mb: see help(memory.size)
>3: In output[[var]][rng] <- df[[var]] :
>Reached total allocation of 3583Mb: see help(memory.size)
>4: In output[[var]][rng] <- df[[var]] :
>Reached total allocation of 3583Mb: see help(memory.size)
>5: In output[[var]][rng] <- df[[var]] :
>Reached total allocation of 3583Mb: see help(memory.size)
>6: In output[[var]][rng] <- df[[var]] :
>Reached total allocation of 3583Mb: see help(memory.size)
>7: In output[[var]][rng] <- df[[var]] :
>Reached total allocation of 3583Mb: see help(memory.size)
>8: In output[[var]][rng] <- df[[var]] :
>Reached total allocation of 3583Mb: see help(memory.size)
>
>
>When I checked -
>
>
>memory.size()
>>[1] 846.83
>
>memory.limit()
>>[1] 3583
>
>
>The code is bit lengthy and unfortunately can't be shared.
>
>Kindly guide how this memory probelm can be tackled? I am using R x64 3.2.0
>
>Regards
>
>Amelia
>
>>________________________________
>
>[email protected] mailing list -- To UNSUBSCRIBE and more, see
>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.
>
______________________________________________
[email protected] mailing list -- To UNSUBSCRIBE and more, see
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.
