[R-sig-eco] Regression with few observations per factor level

[Nicholas Hamilton]

Dear All,

Please do not take any offence, I would really like to be removed from this mailing list, can someone let me know how this can be done.

Best Regards,

--
Nicholas Hamilton
School of Materials Science and Engineering
University of New South Wales (Australia)
--
www.ggtern.com

On 23 Oct 2014, at 10:24 am, Chris Howden <chris at trickysolutions.com.au> wrote:

> A good place to start is by looking at your residuals  to determine if
> the normality assumptions are being met, if not then some form of glm
> that correctly models the residuals or a non parametric method should
> be used.
> 
> But just as important though is considering how you intend to use your
> data and exactly what it is. Irrelevant to what the statistics says if
> you only have 4 datum are you really confident in making broad
> generalisations with it? And writing a paper with your name on it?
> Just a couple datum could change everything, particularly if the scale
> isn't bounded so outliers can have a big impact. If the datum are some
> form of average I would be more confident with only 4 of them, but if
> they are raw values I would consider being very cautious about any
> conclusions you draw.
> 
> Another reason I would be cautious of a result using only 4 datum is
> that their p value estimates may be very poorly estimated. Although
> not widely discussed we often use the Central limit theorem to assume
> parameter estimates are normally distributed when calculating the p
> value. (Because parameters can be thought of as weighted average the
> CLT applies to them). With only 4 datum we can't invoke the magic of
> the CLT and since there is no way to test whether the parameters are
> normal we take quite a risk assuming we have accurate p values at
> small sample sample sizes
> 
> 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 22 Oct 2014, at 17:20, V. Coudrain <v_coudrain at voila.fr> wrote:
> 
>>> With such a small data set, why not simulate some data sets with > reasonable effect sizes and see how an analysis performs? Krzysztof
>> 
>> Dear Krzysztof,
>> It is good idea. Would you know some R functions thatis are well suited for this kind of simulations
>> 
>> 
>> 
>> ___________________________________________________________
>> Mode, hifi, maison,… J'achète malin. Je compare les prix avec
>>   [[alternative HTML version deleted]]
>> 
>> _______________________________________________
>> R-sig-ecology mailing list
>> R-sig-ecology at r-project.org
>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> 
> _______________________________________________
> R-sig-ecology mailing list
> R-sig-ecology at r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology


	[[alternative HTML version deleted]]