[R-sig-ME] modelling seasonal patterns as random effects in a monthly time series

[Chris Howden]

Try lme and lme4. There is also a list calle r-sig-me.



Chris Howden
Founding Partner
Tricky Solutions
Tricky Solutions 4 Tricky Problems
Evidence Based Strategic Development, IP Commercialisation and
Innovation, Data Analysis, Modelling and Training

(mobile) 0410 689 945
(fax / office)
chris at trickysolutions.com.au

Disclaimer: The information in this email and any attachments to it are
confidential and may contain legally privileged information. If you are not
the named or intended recipient, please delete this communication and
contact us immediately. Please note you are not authorised to copy,
use or disclose this communication or any attachments without our
consent. Although this email has been checked by anti-virus software,
there is a risk that email messages may be corrupted or infected by
viruses or other
interferences. No responsibility is accepted for such interference. Unless
expressly stated, the views of the writer are not those of the
company. Tricky Solutions always does our best to provide accurate
forecasts and analyses based on the data supplied, however it is
possible that some important predictors were not included in the data
sent to us. Information provided by us should not be solely relied
upon when making decisions and clients should use their own judgement.

On 16/02/2012, at 8:35, Jianyun Wu <jianyun.fred.wu at gmail.com> wrote:

> Hi Ken,
> I have a monthly time series, and the seasonal pattern is obvious where
> peaks are in Dec and drops in Jan. However due to various "external"
> factors, the seasonal variation during the year may change from time to
> time. The traditional practice here is to use seasonal dummies to treat
> them deterministically.
> Therefore I am curious that instead of treating them as fixed effects,
> whether random effects model can apply for 12 month in each year.
> We do not use Box-Jenkin approach to difference the time series as we can
> hardly find a significant association on a variable of interest. But using
> determinstic trend and seasonal dummies do....
> Thanks
> Fred
>
> On Thu, Feb 16, 2012 at 8:12 AM, Kenneth Frost <kfrost at wisc.edu> wrote:
>
>> Hi, Fred-
>>
>> The answer to your question is probably yes, but you need to provide more
>> details about what you want to do.
>>
>> Ken
>>
>> On 02/15/12, Jianyun Wu   wrote:
>>> Dear All,
>>>
>>> Is there any function or packages in R that I can treat seasonal patterns
>>> as random effects in a monthly time series?
>>>
>>> Is this possible to formulate such a model using nlme, lme4 or other
>>> available mixed model packages?
>>>
>>> Thanks and Regards
>>>
>>> Fred
>>>
>>>     [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>
>   [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models