similar to: Yield to Maturity using R

Dear R helpers   (I have already written the required R code which is giving me correct results for a given single set of data. I just wish to wish to use it for multiple data.)   I have defined a function (as given below) which helps me calculate Macaulay Duration and Modified Duration and its working fine with given set of data.   My Code -   ## ONS - PPA   duration = function(par_value,

# I have written this code in Notepad++ and copied here. ## ONS - PPA    Duration = function(par_value, coupon_rate, freq_coupon, tenure, ytm) { macaulay_duration  =   NULL modified_duration    =   NULL freq_coupon_new    =   NULL if(freq_coupon <= 0) {     freq_coupon_new = 365 } if(freq_coupon > 0 & freq_coupon <= 1) {     freq_coupon_new = 12 } if(freq_coupon > 1 &

Dear R helpers   I have following csv file which is an input   id       par_value    coupon_rate     frequency_coupon   tenure    ytm   1            1000             10                      1                     5          12   # Here frequency_coupon is coded s.t. 0 means Daily compounding, 1 means monthly compouding, 2 means Quarterly, 3 means Half yearly and 4 means only once. Thus in the

Dear R helpers   I am calculating the 'Yield to Maturity' for the Bond with following characteristics.   Its a $1000 face value, 3 year bond with 10% annual coupon and is priced at 101. The yield to maturity can be calculated after solving the equation -   1010 = [100 / (1+ytm)]  + [100 / (1+ytm)^2] + [ 1100 / (1 + ytm)^3]   This can be solved by trial and error method s.t. ytm = 9.601%.

Dear R Helpers I have following data - cash_flow = c(7, 7, 107)  # 107 = Principle 100 + interest of 7% t = c(1,2,3) and zero rate table as rating         year1           year2           year3 AAA            3.60            4.17              4.73 AA              3.65            4.22              4.78 A                 3.72           4.32              4.93 BBB            4.10           

Hi All, Thanks for all your discussions which I read offline and enhance my knowledge. I just want to know how to make yield curve by bootstrapping using R. Please give me some subject-matter links and code links. Especially the Spot Yield Curve and YTM Curve. Sayonara With Smile & With Warm Regards :-) G a u r a v Y a d a v Senior Executive Officer, Economic Research &

This isn't all that likely to be homework, Bert.... However, Alexander, you may find that not many readers are familiar with YTM concepts. There's a chapter with R examples in Ruppert+Matteson's book (if you have SpringerLink, you may be able to download for free). Otherwise you could try searching CRAN, but be warned that you may get considerably more than you wished for. Some

That didn't work Jim! Thanks anyway On Mon, Apr 18, 2016 at 9:02 PM, Jim Lemon <drjimlemon at gmail.com> wrote: > Hi Michael, > At a guess, try this: > > iqr<-function(x) { > return(paste(round(quantile(x,0.25),0),round(quantile(x,0.75),0),sep="-") > } > > .col3_Range=iqr(datat$tenure) > > Jim > > > > On Tue, Apr 19, 2016 at

Hi, I am trying to show an interquartile range while grouping values using the function ddply(). So my function call now is like groupedAll <- ddply(data ,~groupColumn ,summarise ,col1_mean=mean(col1) ,col2_mode=Mode(col2) #Function I wrote for getting the mode shown below

Hello: If the question is how likely an event will occur at a give time point, can we use logistic regression with time t as a predictor variable? For example, if the data is ID Gender Tenure Churn 1 M 17 0 2 M 3 1 3 M 6 0 4 F 10 1 5 F 9 0 6 F

> That didn't work Jim! It always helps to say how the suggestion did not work. Jim's function had a typo in it - was that the problem? Or did you not change the call to ddply to use that function. Here is something that might "work" for you: library(plyr) data <- data.frame(groupColumn=rep(1:5,1:5), col1=2^(0:14)) myIqr <- function(x) {

Hi Michael, At a guess, try this: iqr<-function(x) { return(paste(round(quantile(x,0.25),0),round(quantile(x,0.75),0),sep="-") } .col3_Range=iqr(datat$tenure) Jim On Tue, Apr 19, 2016 at 11:15 AM, Michael Artz <michaeleartz at gmail.com> wrote: > Hi, > I am trying to show an interquartile range while grouping values using > the function ddply(). So my function

To be precise: paste(round(quantile(x,0.25),0),round(quantile(x,0.75),0),sep="-") is an expression that evaluates to a character string: "round(quantile(x,.25),0) - round(quantile(x,0.75),0)" no matter what the argument of your function, x. Hence return(paste(...)) will return this exact character string and never evaluates x. Cheers, Bert Bert Gunter "The

Are you aware that there *already is* a function that does this? ?IQR (also your "function" iqr" is just a character string and would have to be parsed and evaluated to become a function. But this is a TERRIBLE way to do things in R as it completely circumvents R's central functional programming paradigm). Cheers, Bert Bert Gunter "The trouble with having an open mind

... and I'm getting another cup of coffee... -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Apr 19, 2016 at 10:30 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > NO NO -- I am wrong! The paste() expression is

HI that did not work for me either. The value I got returned from that function was "<rounded mean> - <rounded mean>" :(. thanks for the reply through On Tue, Apr 19, 2016 at 10:34 AM, William Dunlap <wdunlap at tibco.com> wrote: > > That didn't work Jim! > > It always helps to say how the suggestion did not work. Jim's > function had a typo

If you show us, not just tell us about, a self-contained example someone might show you a non-hacky way of getting the job done. (I don't see an argument to plyr::ddply called 'transform'.) Bill Dunlap TIBCO Software wdunlap tibco.com On Tue, Apr 19, 2016 at 12:18 PM, Michael Artz <michaeleartz at gmail.com> wrote: > Oh thanks for that clarification Bert! Hope you enjoyed

NO NO -- I am wrong! The paste() expression is of course evaluated. It's just that a character string is returned of the form "something - something". I apologize for the confusion. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County"

Oh thanks for that clarification Bert! Hope you enjoyed your coffee! I ended up just using the transform argument in the ddply function. It worked and it repeated, then I called a mode function in another call to ddply that summarised. Kinda hacky but oh well! On Tue, Apr 19, 2016 at 12:31 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > ... and I'm getting another cup of

Well, instead of your functions try: Mode <- function(x) { tabx <- table(x) tabx[which.max(tabx)] } and use R's IQR function instead of yours. ... so I still don't get why you want to return a character string instead of a value for the IQR; and the mode of a sample defined as above is generally a bad estimator of the mode of the distribution. To say more than that would