Dear all,
This is what I'd like to do (I have an implementation using for loops, which I
designed before I realised just how slow R is at executing them - this process
currently takes days to run).
I have a large dataframe containing corporate bond data, columns are:
BondID
Date (goes back 5years)
Var1
Var2
Term2Maturity
What I want to do is this:
1) For each bond, at each given date, look back over 1 year and append
some statistics to each row ( sd(Var1), cor(Var1,Var2) over that year etc)
a. It seems I might be able to use ddply for this, but I can't work out
how to code the stats function to only look back over one year, rather than the
full data range
b. For example: dfBondsWithCorr<-ddply(dfBonds, .(BondID),
transform,corr=cor(Var1,Var2),.progress="text")
returns a dataframe where for each bond it has same corr for each date
2) On each date, subset dfBondsWithCorr by certain qualification criteria,
then to the qualifiers fit a regression through a Var1 and Term2Maturity,
output the regression as a df of curves (say for each date, a curve represented
by points every 0.5 years)
a. I can do this pretty efficiently for a single date (and I suppose I
could wrap that in a function) , but can't quite see how to do the filtering
and spitting out of curves over multiple dates without using for loops
Would appreciate any thoughts, many thanks in advance
Russ
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