[R] how to interpolate time series data with missingness

[Matthew Keller]

Excellent. Thank you. I have been messing around with loess() but this
looks quicker. Thanks,

Matt

On Wed, Jun 17, 2009 at 4:07 PM, Gabor
Grothendieck<ggrothendieck at gmail.com> wrote:
> The zoo package has a number of na.* routines:
>
>> library(zoo)
>> x <- c(2,3,NA,NA,NA,3.2,3.5,NA,NA,6,NA)
>> na.approx(x)
>  [1] 2.000000 3.000000 3.050000 3.100000 3.150000 3.200000 3.500000 4.333333
>  [9] 5.166667 6.000000
>> na.locf(x)
>  [1] 2.0 3.0 3.0 3.0 3.0 3.2 3.5 3.5 3.5 6.0 6.0
>> na.spline(x)
>  [1] 2.000000 3.000000 3.366531 3.352065 3.211566 3.200000 3.500000 4.045127
>  [9] 4.857627 6.000000 7.534746
>
>
> On Wed, Jun 17, 2009 at 5:54 PM, Matthew Keller<mckellercran at gmail.com> wrote:
>> Hi all,
>>
>> I have a vector, most of which is missing. The data is always
>> increasing, but may do so in jumps. I would like to interpolate the
>> NAs with 'best guesses', using something like filter(), which doesn't
>> work due to the NAs. Here is an example:
>>
>>> x <- c(2,3,NA,NA,NA,3.2,3.5,NA,NA,6,NA)
>>> x
>>  [1] 2.0 3.0  NA  NA  NA 3.2 3.5  NA  NA 6.0  NA
>>
>> I would like a function that would take the NAs and fill in the
>> average values around the NAs. E.g., make a new vector x.new that
>> looks like:
>>> x.new
>> [1] 2.0 3.0 3.1 3.1 3.1 3.2 3.5 4.75 4.75 6 6
>>
>> Or, alternatively, that could figure out a more likely value than just
>> the average. There must be something simple I'm overlooking, like some
>> kind of loess y-hat or something? Any help would be appreciated,
>>
>> Matt
>>
>> --
>> Matthew C Keller
>> Asst. Professor of Psychology
>> University of Colorado at Boulder
>> www.matthewckeller.com
>>
>> ______________________________________________
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>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>



-- 
Matthew C Keller
Asst. Professor of Psychology
University of Colorado at Boulder
www.matthewckeller.com