On Wed, 3 Mar 2010, Research wrote:
Hello,
I have various datasets of zoo time series (merged into single frames
via the "merge" command) such as
date var0 var1 var2 var3
08/07/1996 652.54 0.223922 0.515819 0.502638
08/08/1996 662.59 0.997841 0.000383 0.999806
06/09/1996 655.68 0.901685 0.569763 0.866333
08/10/1996 700.64 0.268855 0.244701 0.329285
08/11/1996 730.82 0.438407 0.501427 0.461374
06/12/1996 739.6 0.432233 0.562175 0.052423
08/01/1997 748.41 0.616211 0.799211 5.96E-09
07/02/1997 789.56 0.378415 0.645088 0.93862
07/03/1997 804.97 0.142706 0.422156 0.145648
08/04/1997 766.12 0.999999 0.999435 0.998576
08/05/1997 820.26 0.810795 0.966044 0.000427
06/06/1997 858.01 0.998652 0.998287 0.994222
08/07/1997 918.75 0.951553 0.974251 0.89633
I am trying to put all these into a sort of a list so I can invoke them
via the list
and not individually so I can run a batch of statistical analyses on them.
I also want to have the specification of the analysis on a list of some
sort as well.
For example:
List "a" has 3 zoo data sets and list "b" has 2 specifications (calls)
for a function (say, "analysis(specification, inputdata)" ) that runs
some statistical manipulations (regressions, nonlin models, etc. etc.)
on the elements of the zoo data frames. Thus I want to be able to run
(this is pseudo R code):
for (i in 1:3)
{
output1<-analysis(b[1], a[i])
output2<-analysis(b[2], a[i])
}
Thanks in advance for any pointers or help.
Not sure how exactly you want to organize these, but assume you have a
multivariate zoo series like:
z <- structure(c(0.4, -0.84, 0.92, 0.41, -0.47, -0.07, 0.36, -1.48,
-1.39, 0.53, -0.65, -0.7), .Dim = c(4L, 3L), .Dimnames = list(
NULL, c("a", "b", "c")), index = 1:4, class = "zoo")
Then you can easily compute column-wise (i.e., variable-wise) such as
sapply(z, mean)
Or if you want to apply a list of functions
lapply(list(mean, sd), function(f) sapply(z, f))
etc.
hth,
Z
Best,
Costas
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