Re: [Rd] Multivariate time series in R 3 vs R 2

2013-10-27 Thread 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 ggrothendi...@gmail.com

 On Wed, Oct 23, 2013 at 2:56 PM, Андрей Парамонов 
 cmr.p...@gmail.com
 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: [Rd] Multivariate time series in R 3 vs R 2

2013-10-26 Thread 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 plumm...@iarc.fr

 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

Re: [Rd] Multivariate time series in R 3 vs R 2

2013-10-26 Thread Gabor Grothendieck 
On Wed, Oct 23, 2013 at 2:56 PM, Андрей Парамонов cmr.p...@gmail.com 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.

__
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel

Re: [Rd] Multivariate time series in R 3 vs R 2

2013-10-25 Thread Martyn Plummer 
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

[Rd] Multivariate time series in R 3 vs R 2

2013-10-24 Thread 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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