Multivariate time series in R 3 vs R 2

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Multivariate time series in R 3 vs R 2

Андрей Парамонов
Hello!

Recently I got report that my package mar1s doesn't pass checks any more on
R 3.0.2. I started to investigate and found the following difference in
multivariate time series handling in R 3.0.2 compared to R 2 (I've checked
on 2.14.0).

Suppose I wish to calculate seasonal component for time series. In case of
multivariate time series, I wish to process each column independently. Let
f be a simple (trivial) model of seasonal component:

f <- function(x)
  return(ts(rep(0, length(x)), start = 0, frequency = frequency(x)))

In previous versions of R, I used the following compact and efficient
expression to calculate seasonal component:

y <- do.call(cbind, lapply(x, f))

It worked equally good for univariate and multivariate time series:

> R.Version()$version.string
[1] "R version 2.14.0 (2011-10-31)"
> t <- ts(1:10, start = 100, frequency = 10)
>
> x <- t
> y <- do.call(cbind, lapply(x, f))
> y
Time Series:
Start = c(0, 1)
End = c(0, 10)
Frequency = 10
 [1] 0 0 0 0 0 0 0 0 0 0
>
> x <- cbind(t, t)
> y <- do.call(cbind, lapply(x, f))
> y
Time Series:
Start = c(0, 1)
End = c(0, 10)
Frequency = 10
    t t
0.0 0 0
0.1 0 0
0.2 0 0
0.3 0 0
0.4 0 0
0.5 0 0
0.6 0 0
0.7 0 0
0.8 0 0
0.9 0 0

But in version 3, I get some frustrating results:

> R.Version()$version.string
[1] "R version 3.0.2 (2013-09-25)"
> t <- ts(1:10, start = 100, frequency = 10)
>
> x <- t
> y <- do.call(cbind, lapply(x, f))
> y
Time Series:
Start = 0
End = 0
Frequency = 1
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
>
> x <- cbind(t, t)
> y <- do.call(cbind, lapply(x, f))
> y
Time Series:
Start = 0
End = 0
Frequency = 1
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0
  structure(0, .Tsp = c(0, 0, 1), class = "ts")
0                                             0

I didn't watch R development for quite some time now. Could anyone please
help me to construct similar expression to what I have used in R 2, for
multivariate case (or better, for both univariate and multivariate cases)?

Best wishes,
Andrey Paramonov

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Re: Multivariate time series in R 3 vs R 2

Martyn Plummer-3
This has nothing to do with changes in base R. It is due to changes in
the dependent packages. These changes mean that when you call lapply it
does not dispatch the right as.list method.

The method you want (as.list.ts) is provided by the zoo package. It
splits a multivariate time series into a list of univariate time series
in the way you are expecting.  Your package mar1s used to depend on zoo
indirectly through the fda package. But now fda does not depend on zoo,
it only suggests it. So now, when you load your package, zoo is not on
the search path and you get the default as.list method, which produces
the bad results.

The solution is to add "Imports: zoo" to your DESCRIPTION file and
"import(zoo)" to your NAMESPACE file.

Martyn


On Wed, 2013-10-23 at 22:56 +0400, Андрей Парамонов wrote:

> Hello!
>
> Recently I got report that my package mar1s doesn't pass checks any more on
> R 3.0.2. I started to investigate and found the following difference in
> multivariate time series handling in R 3.0.2 compared to R 2 (I've checked
> on 2.14.0).
>
> Suppose I wish to calculate seasonal component for time series. In case of
> multivariate time series, I wish to process each column independently. Let
> f be a simple (trivial) model of seasonal component:
>
> f <- function(x)
>   return(ts(rep(0, length(x)), start = 0, frequency = frequency(x)))
>
> In previous versions of R, I used the following compact and efficient
> expression to calculate seasonal component:
>
> y <- do.call(cbind, lapply(x, f))
>
> It worked equally good for univariate and multivariate time series:
>
> > R.Version()$version.string
> [1] "R version 2.14.0 (2011-10-31)"
> > t <- ts(1:10, start = 100, frequency = 10)
> >
> > x <- t
> > y <- do.call(cbind, lapply(x, f))
> > y
> Time Series:
> Start = c(0, 1)
> End = c(0, 10)
> Frequency = 10
>  [1] 0 0 0 0 0 0 0 0 0 0
> >
> > x <- cbind(t, t)
> > y <- do.call(cbind, lapply(x, f))
> > y
> Time Series:
> Start = c(0, 1)
> End = c(0, 10)
> Frequency = 10
>     t t
> 0.0 0 0
> 0.1 0 0
> 0.2 0 0
> 0.3 0 0
> 0.4 0 0
> 0.5 0 0
> 0.6 0 0
> 0.7 0 0
> 0.8 0 0
> 0.9 0 0
>
> But in version 3, I get some frustrating results:
>
> > R.Version()$version.string
> [1] "R version 3.0.2 (2013-09-25)"
> > t <- ts(1:10, start = 100, frequency = 10)
> >
> > x <- t
> > y <- do.call(cbind, lapply(x, f))
> > y
> Time Series:
> Start = 0
> End = 0
> Frequency = 1
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
> >
> > x <- cbind(t, t)
> > y <- do.call(cbind, lapply(x, f))
> > y
> Time Series:
> Start = 0
> End = 0
> Frequency = 1
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>
> I didn't watch R development for quite some time now. Could anyone please
> help me to construct similar expression to what I have used in R 2, for
> multivariate case (or better, for both univariate and multivariate cases)?
>
> Best wishes,
> Andrey Paramonov
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel

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Re: Multivariate time series in R 3 vs R 2

Андрей Парамонов
Thank you for your suggestions.
But in minimal example I provided I didn't import any package (incl. zoo).
However the behavior is different. Something in R has changed between
2.14.0 and 3.0.2.

Best wishes,
Andrey Paramonov


2013/10/25 Martyn Plummer <[hidden email]>

> This has nothing to do with changes in base R. It is due to changes in
> the dependent packages. These changes mean that when you call lapply it
> does not dispatch the right as.list method.
>
> The method you want (as.list.ts) is provided by the zoo package. It
> splits a multivariate time series into a list of univariate time series
> in the way you are expecting.  Your package mar1s used to depend on zoo
> indirectly through the fda package. But now fda does not depend on zoo,
> it only suggests it. So now, when you load your package, zoo is not on
> the search path and you get the default as.list method, which produces
> the bad results.
>
> The solution is to add "Imports: zoo" to your DESCRIPTION file and
> "import(zoo)" to your NAMESPACE file.
>
> Martyn
>
>
> On Wed, 2013-10-23 at 22:56 +0400, Андрей Парамонов wrote:
> > Hello!
> >
> > Recently I got report that my package mar1s doesn't pass checks any more
> on
> > R 3.0.2. I started to investigate and found the following difference in
> > multivariate time series handling in R 3.0.2 compared to R 2 (I've
> checked
> > on 2.14.0).
> >
> > Suppose I wish to calculate seasonal component for time series. In case
> of
> > multivariate time series, I wish to process each column independently.
> Let
> > f be a simple (trivial) model of seasonal component:
> >
> > f <- function(x)
> >   return(ts(rep(0, length(x)), start = 0, frequency = frequency(x)))
> >
> > In previous versions of R, I used the following compact and efficient
> > expression to calculate seasonal component:
> >
> > y <- do.call(cbind, lapply(x, f))
> >
> > It worked equally good for univariate and multivariate time series:
> >
> > > R.Version()$version.string
> > [1] "R version 2.14.0 (2011-10-31)"
> > > t <- ts(1:10, start = 100, frequency = 10)
> > >
> > > x <- t
> > > y <- do.call(cbind, lapply(x, f))
> > > y
> > Time Series:
> > Start = c(0, 1)
> > End = c(0, 10)
> > Frequency = 10
> >  [1] 0 0 0 0 0 0 0 0 0 0
> > >
> > > x <- cbind(t, t)
> > > y <- do.call(cbind, lapply(x, f))
> > > y
> > Time Series:
> > Start = c(0, 1)
> > End = c(0, 10)
> > Frequency = 10
> >     t t
> > 0.0 0 0
> > 0.1 0 0
> > 0.2 0 0
> > 0.3 0 0
> > 0.4 0 0
> > 0.5 0 0
> > 0.6 0 0
> > 0.7 0 0
> > 0.8 0 0
> > 0.9 0 0
> >
> > But in version 3, I get some frustrating results:
> >
> > > R.Version()$version.string
> > [1] "R version 3.0.2 (2013-09-25)"
> > > t <- ts(1:10, start = 100, frequency = 10)
> > >
> > > x <- t
> > > y <- do.call(cbind, lapply(x, f))
> > > y
> > Time Series:
> > Start = 0
> > End = 0
> > Frequency = 1
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> > >
> > > x <- cbind(t, t)
> > > y <- do.call(cbind, lapply(x, f))
> > > y
> > Time Series:
> > Start = 0
> > End = 0
> > Frequency = 1
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >
> > I didn't watch R development for quite some time now. Could anyone please
> > help me to construct similar expression to what I have used in R 2, for
> > multivariate case (or better, for both univariate and multivariate
> cases)?
> >
> > Best wishes,
> > Andrey Paramonov
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > [hidden email] mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-devel
>
>
        [[alternative HTML version deleted]]


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Re: Multivariate time series in R 3 vs R 2

Gabor Grothendieck
In reply to this post by Андрей Парамонов
On Wed, Oct 23, 2013 at 2:56 PM, Андрей Парамонов <[hidden email]> wrote:

> Hello!
>
> Recently I got report that my package mar1s doesn't pass checks any more on
> R 3.0.2. I started to investigate and found the following difference in
> multivariate time series handling in R 3.0.2 compared to R 2 (I've checked
> on 2.14.0).
>
> Suppose I wish to calculate seasonal component for time series. In case of
> multivariate time series, I wish to process each column independently. Let
> f be a simple (trivial) model of seasonal component:
>
> f <- function(x)
>   return(ts(rep(0, length(x)), start = 0, frequency = frequency(x)))
>
> In previous versions of R, I used the following compact and efficient
> expression to calculate seasonal component:
>
> y <- do.call(cbind, lapply(x, f))
>
> It worked equally good for univariate and multivariate time series:
>
>> R.Version()$version.string
> [1] "R version 2.14.0 (2011-10-31)"
>> t <- ts(1:10, start = 100, frequency = 10)
>>
>> x <- t
>> y <- do.call(cbind, lapply(x, f))
>> y
> Time Series:
> Start = c(0, 1)
> End = c(0, 10)
> Frequency = 10
>  [1] 0 0 0 0 0 0 0 0 0 0
>>
>> x <- cbind(t, t)
>> y <- do.call(cbind, lapply(x, f))
>> y
> Time Series:
> Start = c(0, 1)
> End = c(0, 10)
> Frequency = 10
>     t t
> 0.0 0 0
> 0.1 0 0
> 0.2 0 0
> 0.3 0 0
> 0.4 0 0
> 0.5 0 0
> 0.6 0 0
> 0.7 0 0
> 0.8 0 0
> 0.9 0 0
>
> But in version 3, I get some frustrating results:
>
>> R.Version()$version.string
> [1] "R version 3.0.2 (2013-09-25)"
>> t <- ts(1:10, start = 100, frequency = 10)
>>
>> x <- t
>> y <- do.call(cbind, lapply(x, f))
>> y
> Time Series:
> Start = 0
> End = 0
> Frequency = 1
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> 0                                             0
>>


I get the same results in R-2.14.0 and R-3.02.  They both give the
result shown above with the structures in the output.  I used
"R version 2.14.0 (2011-10-31)".

Try starting a clean session in R 2.14.0 using:

R --vanilla

and try it again.

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Re: Multivariate time series in R 3 vs R 2

Андрей Парамонов
Ah, indeed. I must have run R 2.14.0 with library zoo loaded. Very sorry
for the noise.

Thank you for your patience,
Andrey Paramonov



2013/10/26 Gabor Grothendieck <[hidden email]>

> On Wed, Oct 23, 2013 at 2:56 PM, Андрей Парамонов <[hidden email]>
> wrote:
> > Hello!
> >
> > Recently I got report that my package mar1s doesn't pass checks any more
> on
> > R 3.0.2. I started to investigate and found the following difference in
> > multivariate time series handling in R 3.0.2 compared to R 2 (I've
> checked
> > on 2.14.0).
> >
> > Suppose I wish to calculate seasonal component for time series. In case
> of
> > multivariate time series, I wish to process each column independently.
> Let
> > f be a simple (trivial) model of seasonal component:
> >
> > f <- function(x)
> >   return(ts(rep(0, length(x)), start = 0, frequency = frequency(x)))
> >
> > In previous versions of R, I used the following compact and efficient
> > expression to calculate seasonal component:
> >
> > y <- do.call(cbind, lapply(x, f))
> >
> > It worked equally good for univariate and multivariate time series:
> >
> >> R.Version()$version.string
> > [1] "R version 2.14.0 (2011-10-31)"
> >> t <- ts(1:10, start = 100, frequency = 10)
> >>
> >> x <- t
> >> y <- do.call(cbind, lapply(x, f))
> >> y
> > Time Series:
> > Start = c(0, 1)
> > End = c(0, 10)
> > Frequency = 10
> >  [1] 0 0 0 0 0 0 0 0 0 0
> >>
> >> x <- cbind(t, t)
> >> y <- do.call(cbind, lapply(x, f))
> >> y
> > Time Series:
> > Start = c(0, 1)
> > End = c(0, 10)
> > Frequency = 10
> >     t t
> > 0.0 0 0
> > 0.1 0 0
> > 0.2 0 0
> > 0.3 0 0
> > 0.4 0 0
> > 0.5 0 0
> > 0.6 0 0
> > 0.7 0 0
> > 0.8 0 0
> > 0.9 0 0
> >
> > But in version 3, I get some frustrating results:
> >
> >> R.Version()$version.string
> > [1] "R version 3.0.2 (2013-09-25)"
> >> t <- ts(1:10, start = 100, frequency = 10)
> >>
> >> x <- t
> >> y <- do.call(cbind, lapply(x, f))
> >> y
> > Time Series:
> > Start = 0
> > End = 0
> > Frequency = 1
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >   structure(0, .Tsp = c(0, 0, 1), class = "ts")
> > 0                                             0
> >>
>
>
> I get the same results in R-2.14.0 and R-3.02.  They both give the
> result shown above with the structures in the output.  I used
> "R version 2.14.0 (2011-10-31)".
>
> Try starting a clean session in R 2.14.0 using:
>
> R --vanilla
>
> and try it again.
>
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