Dear Mr Adams,
Thanks a lot for your solution. I understand it was very tricky and needed lot
of application. Thanks again and do appreciate your efforts.
Regards
Katherine
--- On Thu, 25/4/13, Adams, Jean <jvad...@usgs.gov> wrote:
From: Adams, Jean <jvad...@usgs.gov>
Subject: Re: [R] Linear Interpolation : Missing rates
To: "Katherine Gobin" <katherine_go...@yahoo.com>
Cc: "R help" <r-help@r-project.org>
Date: Thursday, 25 April, 2013, 2:23 PM
Katherine,
Split the rate names into their currency and tenor parts and assign a numeric
value to each tenor. Choose a model to do your approximations (I used linear
regression in the example below). Use this model to generate estimates for all
combinations of currency and tenor.
For example:
# split the rate names into currency and tenorsplitnames <- do.call(rbind,
strsplit(df$rate_name, "_"))df$currency <- as.factor(splitnames[, 1])
df$tenor <- splitnames[, 2]
# assign numeric value to each tenoruniquetenors <- c("1w", "2w", "1m",
"2m")uniquedays <- c(7, 14, 30.5, 61)
df$tenordays <- uniquedays[match(df$tenor, uniquetenors)]
# fit a linear model of rate on tenordays for each currencyfit <- lm(rates ~
currency*tenordays, data=df)
# estimate rates for all combinations of currency and tenorfulldf <-
expand.grid(tenordays=unique(df$tenordays),
currency=unique(df$currency))fulldf$est.rates = predict(fit, newdata=fulldf)
# merge observed rates with estimated ratesdfwithest <- merge(df, fulldf,
all=TRUE)
Jean
On Thu, Apr 25, 2013 at 12:33 AM, Katherine Gobin <katherine_go...@yahoo.com>
wrote:
Dear R forum
I have data.frame as
df = data.frame(rate_name = c("USD_1w", "USD_1w", "USD_1w", "USD_1w", "USD_1m",
"USD_1m", "USD_1m", "USD_1m", "USD_2m", "USD_2m", "USD_2m", "USD_2m",
"GBP_1w", "GBP_1w", "GBP_1w", "GBP_1w", "GBP_1m", "GBP_1m", "GBP_1m", "GBP_1m",
"GBP_2m", "GBP_2m", "GBP_2m", "GBP_2m", "EURO_1w", "EURO_1w", "EURO_1w",
"EURO_1w", "EURO_2w", "EURO_2w", "EURO_2w", "EURO_2w", "EURO_2m", "EURO_2m",
"EURO_2m", "EURO_2m"), rates = c(2.05, 2.07, 2.06, 2.06, 2.22, 2.24, 2.23,
2.23, 2.31, 2.33, 2.33, 2.31, 1.06, 1.08, 1.08, 1.08, 1.21, 1.21, 1.23, 1.21,
1.41, 1.39, 1.39, 1.37, 1.82, 1.82, 1.81, 1.80, 1.98, 1.98, 1.97, 1.97, 2.1,
2.09, 2.09, 2.11))
currency = c("EURO", "GBP", "USD")
tenor = c("1w", "2w", "1m", "2m", "3m")
# _________________________________________
> df
rate_name rates
rate_name rates
1 USD_1w 2.05
2 USD_1w 2.07
3 USD_1w 2.06
4 USD_1w 2.06
5 USD_1m 2.22
6 USD_1m 2.24
7 USD_1m 2.23
8 USD_1m 2.23
9 USD_2m 2.31
10 USD_2m 2.33
11 USD_2m 2.33
12 USD_2m 2.31
13 GBP_1w 1.06
14 GBP_1w 1.08
15 GBP_1w 1.08
16 GBP_1w 1.08
17 GBP_1m 1.21
18 GBP_1m 1.21
19 GBP_1m 1.23
20 GBP_1m 1.21
21 GBP_2m 1.41
22 GBP_2m 1.39
23 GBP_2m 1.39
24 GBP_2m 1.37
25 EURO_1w 1.82
26 EURO_1w 1.82
27 EURO_1w 1.81
28 EURO_1w 1.80
29 EURO_2w 1.98
30 EURO_2w 1.98
31 EURO_2w 1.97
32 EURO_2w 1.97
33 EURO_2m 2.10
34 EURO_2m 2.09
35 EURO_2m 2.09
36 EURO_2m 2.11
As can be seen that USD_2w, GBP_2w and EURO_1m are missing and I need to
INTERPOLATE these rates, which can be done using approx or approxfun. In
reality I can have many currencies with many tenors. Problem is when the
data.frame "df" is read or accessed in R, I am not aware which tenor is
missing. For a given currency, it is possible that mare than 1 consecutive
tenors may be missing e.g. in case of EURO, I may have EURO_1w, EURO_2w and
then EURO_4m. So EURO_1m, EURO_2m and EURO_3m are missing.
I understand it's sort of vague question from me and do apologize for the same.
Any suggestion please.
Regards
Katherine
[[alternative HTML version deleted]]
______________________________________________
R-help@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.
[[alternative HTML version deleted]]
______________________________________________
R-help@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.
