[R] Random assignment

Fri Oct 15 14:34:22 CEST 2010

Hi John,

I haven't read that particular paper but in answer to your question...

> So if i do this for all the families it will be the same as doing the simulation experiment
> outline in the method above?

Yes :)

Michael

On 15 October 2010 23:18, John Haart <another83 at me.com> wrote:
> Hi Michael,
>
> Thanks for this - the reason i am following this approach is that it appeared in a paper i was reading, and i thought it was a interesting angle to take
>
> The paper is
>
> Vamosi & Wilson, 2008. Nonrandom extinction leads to elevated loss of angiosperm evolutionary history. Ecology Letters, (2008) 11: 1047–1053.
>
> and the specific method i am following states :-
>
>> We calculated the number of species expected to be at risk in each family under a random binomial distribution in 10 000 randomizations [generated using R version 2.6.0 (R Development Team 2007)] assuming every species has a 7.48% chance of being at risk.
>
> I guess the reason i am doing the simulation is because i am not hugely statistically minded and the paper was asking the same question i am interested in answering :).
>
> So following your approach -
>
>> if family F has Fn species, your random expectation is that p * Fn of
>> them will be at risk (p = 0.0748). The variance on that expectation
>> will be p * (1-p) * Fn.
>
>
> Family f = Bromeliaceae , with Fn = 80, p=0.0748
> random expectation = p*Fn = (0.0748*80) = 5.984
> variance = p * (1-p) * Fn = (0.0748*0.9252) *80 = 5.5363968
>
> So the random expectation is that the Bromeliaceae will have 6 species at risk, if risk is assigned randomly?
>
> So if i do this for all the families it will be the same as doing the simulation experiment outline in the method above?
>
> Thanks
>
> John
>
>
>
>
> On 15 Oct 2010, at 12:49, Michael Bedward wrote:
>
> Hi John,
>
> The word "species" attracted my attention :)
>
> Like Dennis, I'm not sure I understand your idea properly. In
> particular, I don't see what you need the simulation for.
>
> If family F has Fn species, your random expectation is that p * Fn of
> them will be at risk (p = 0.0748). The variance on that expectation
> will be p * (1-p) * Fn.
>
> If you do your simulation that's the result you'll get.  Perhaps to
> initial identify families with disproportionate observed extinction
> rates all you need is the dbinom function ?
>
> Michael
>
>
> On 15 October 2010 22:29, John Haart <another83 at me.com> wrote:
>> Hi Denis and list
>>
>> Thanks for this , and sorry for not providing enough information
>>
>> First let me put the study into a bit more context : -
>>
>> I know the number of species at risk in each family, what i am asking  is "Is risk random according to family or do certain families have a disproportionate number of at risk species?"
>>
>> My idea was to randomly allocate risk to the families based on the criteria below (binomial(nspecies, 0.0748)) and then compare this to the "true data" and see if there was a significant difference.
>>
>> So in answer to your questions, (assuming my method is correct !)
>>
>>> Is this over all families, or within a particular family? If the former, why
>>> does a distinction of family matter?
>>
>> Within a particular family  - this is because i am looking to see if risk in the "observed" data set is random in respect to family so this will provide the baseline to compare against.
>>
>>> I guess you've stated the p, but what's the n? The number of species in each
>>> family?
>>
>> This varies largely, for instance i have some families that are monotypic  (with 1 species) and then i have other families with 100+ species
>>
>>
>>> Assuming you have multiple families, do you want separate simulations per
>>> family, or do you want to do some sort of weighting (perhaps proportional to
>>> size) over all families?
>>
>> I am assuming i want some sort of weighting. This is because i am wanting to calculate the number of species expected to be at risk in EACH family under the random binomial distribution ( assuming every species has a 7.48% chance of being at risk.
>>
>> Thanks
>>
>> John
>>
>>
>>
>>
>> On 15 Oct 2010, at 11:19, Dennis Murphy wrote:
>>
>> Hi:
>>
>> I don't believe you've provided quite enough information just yet...
>>
>> On Fri, Oct 15, 2010 at 2:22 AM, John Haart <another83 at me.com> wrote:
>>
>>> Dear List,
>>>
>>> I am doing some simulation in R and need basic help!
>>>
>>> I have a list of animal families for which i know the number of species in
>>> each family.
>>>
>>> I am working under the assumption that a species has a 7.48% chance of
>>> being at risk.
>>>
>>
>> Is this over all families, or within a particular family? If the former, why
>> does a distinction of family matter?
>>
>>>
>>> I want to simulate the number of species expected to be at risk under a
>>> random binomial distribution with 10,000 randomizations.
>>>
>>
>> I guess you've stated the p, but what's the n? The number of species in each
>> family? If you're simulating on a family by family basis, then it would seem
>> that a binomial(nspecies, 0.0748) distribution would be the reference.
>> Assuming you have multiple families, do you want separate simulations per
>> family, or do you want to do some sort of weighting (perhaps proportional to
>> size) over all families? The latter is doable, but it would require a
>> two-stage simulation: one to randomly select a family and then to randomly
>> select a species.
>>
>> Dennis
>>
>>
>>>
>>> I am relatively knew to this field and would greatly appreciate a
>>> "idiot-proof" response, I.e how should the data be entered into R? I was
>>> thinking of using read.table, header = T, where the table has F = Family
>>> Name, and SP = Number of species in that family?
>>>
>>> John
>>>
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>>
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>>
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>> and provide commented, minimal, self-contained, reproducible code.
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
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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.
>>
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>