Dear Uwe and David,
Yes, definitely i was wrong. The expression in R should be:
glm(cbind(FD, 12 - FD) ~ Fsize, family=binomial, data=subFS)
----
Call: glm(formula = cbind(FD, 12 - FD) ~ Fsize, family = binomial,
data = subFS)
Coefficients:
(Intercept) Fsize
0.6381 -0.1203
Degrees of Freedom: 29 Total (i.e. Null); 28 Residual
Null Deviance: 193.3
Residual Deviance: 179.9 AIC: 245.1
----
(the direction of Fsize is as expected).
I am not sure with your second statement mentioning that "R can deal with
perfect separation". Despite of convergence issue, does R take into
account zero or infinite odds value or leave them to calculate the
parameters? How? I need to know the basic of this calculation since so far
I haven't found any literature discuss this problem and the way to handle
this (well, someone need to understand this as well :) )
David, i'm thinking to use LDA as well but i cannot comment this time.
Thanks for any clarification.
Best,
Wim
Research Officer
CIFOR-Indonesia
On Tue, Dec 13, 2011 at 4:08 AM, David Winsemius <dwinsem...@comcast.net>wrote:
>
> On Dec 12, 2011, at 3:51 PM, Uwe Ligges wrote:
>
> 1. The formula you used is not for a logistic but an ordinal regression
>> (since you are using the default gaussian family rather than
>> family="binomial" or whatever.
>>
>
> this this then produce one version of the "Armitage linear test of trend"?
>
>
>
>> 2. R (nor any other software) can deal with perfect separation (nor
>> quasi-separation) of classes, since the problem is not well defined in such
>> a case as you found out already. R will give a warning in that case, that
>> the Fisher Scoring does not converge.
>>
>> LDA will give perfect results in such a case (well, unless the within
>> class covariance matrix is singular).
>>
>> Best,
>> Uwe Ligges
>>
>>
>>
>> On 12.12.2011 11:46, wim nursal wrote:
>>
>>> Dear statistician experts,
>>>
>>> Sorry if this is a trivial question, or the old same question (i don't
>>> know
>>> what is the efficient key word for this issue).
>>> In order to understand the calculation of parameter of logistic
>>> regression,
>>> I did an exercise through spreadsheet following the procedural example
>>> from a literature, or the available spreadsheet (with calculation
>>> formula).
>>> I ended up with infinity (divided by zero) when the odd ratio is exactly
>>> 1
>>> (FD=12) or invalid number when odd ratio is zero (MFD = 0) after log.
>>> I am wondering how R through GLM function (particularly logit or
>>> logistic
>>> regression) treats the odds ratios or log odd ratios that is exatcly one
>>> or
>>> zeros.
>>>
>>> The sample data is like this:
>>> #HH Fsize FD
>>> 1 1.29472 0
>>> 2 1.6184 0
>>> 3 2.4276 1
>>> 4 2.4276 2
>>> 5 20.23 2
>>> 6 1.6184 3
>>> 7 1.820 3
>>> 8 0.4046 3
>>> 9 6.069 4
>>> 10 2.6299 4
>>> 11 0.72828 5
>>> 12 2.4276 5
>>> 13 6.069 7
>>> 14 4.8552 7
>>> 15 2.32645 7
>>> 16 1.6184 8
>>> 17 1.0115 8
>>> 18 1.0115 8
>>> 19 5.2598 9
>>> 20 2.023 10
>>> 21 0.6069 10
>>> 22 1.2138 11
>>> 23 0.8092 11
>>> 24 1.4161 11
>>> 25 0.6069 11
>>> 26 3.440 11
>>> 27 1.2138 12
>>> 28 1.2138 12
>>> 29 0.4046 12
>>> 30 1.2138 12
>>>
>>> Fsize is the farm size (acre or hectare). Food deficit (FD) is the
>>> number
>>> of months (last year from the survey took place) that an household had
>>> bought food-grains (minimum = 0 month, maximum = 12 months or whole year
>>> deficit).
>>> Even though I "jitter"-ed the minimum or maximum FD value only (eg.
>>> FD=0+1e-6 or FD=12-1e-6), nothing changed to the result.
>>>
>>> The formula I used is like this:
>>> ------------------------------**------------------------------**--
>>> glm(FD ~ Fsize, data = subFS)
>>> --
>>> Coefficients:
>>> (Intercept) Fsize
>>> 7.7913 -0.3092
>>>
>>> Degrees of Freedom: 29 Total (i.e. Null); 28 Residual
>>> Null Deviance: 463
>>> Residual Deviance: 425.5 AIC: 170.7
>>> ------------------------------**------------------------------**--
>>>
>>> I appreciate for any clarification.
>>>
>>> Best wishes,
>>> Wim
>>>
>>>
>>>
> David Winsemius, MD
> Heritage Laboratories
> West Hartford, CT
>
>
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