[R] Logistic regression problem: propensity score matching

[John Fox]

Dear Paul,


At 08:41 PM 6/4/2003 +0100, Paul wrote:
>Thanks for your reply.
>
>I am using logistic regression because my response variable is categorical 
>- and this seems to be recommended in the literature (by Heckman, Smith 
>and others).

I think that Prof. Ripley's point here is that although one can use multnom 
in the nnet package to fit a binary (or binomial) logistic regression, it 
is more common to do so using the glm (generlized linear model) function. 
One normally would use multinomial logistic regression only for a 
polytomous (several-category) response variable. Applied to a dichotomous 
response, it will give the same results as a binary logistic regression.

>. . .
>
>I have MASS but was unable to locate logistic regression, which I was 
>advised was the standard method for my problem.

In MASS (4th edition), logit models are discussed in chapter 7 on 
generalized linear models (see, in particular, section 7.2). In my R and 
S-PLUS Companion, to which you referred in your original message, these 
models are discussed in chapter 5 on generalized linear models (see, in 
particular, section 5.2.1).

I hope that this helps,
  John

>Thanks again.
>
>Prof Brian Ripley wrote:
>
>>1) Why are you using multinom when this is not a multinomial logistic 
>>regression?  You could just use a binomial glm.
>>
>>2) The second argument to predict() is `newdata'.  `sample' is an R 
>>function, so what did you mean to have there?  I think the predictions 
>>should be a named vector if `sample' is a data frame.
>>
>>3) There are many more examples of such things (and more explanation) in 
>>Venables & Ripley's MASS (the book).
>>
>>On Wed, 4 Jun 2003, Paul Bivand wrote:
>>
>>
>>
>>>I am doing one part of an evaluation of a mandatory welfare-to-work 
>>>programme in the UK.
>>>As with all evaluations, the problem is to determine what would have 
>>>happened if the initiative had not taken place.
>>>In our case, we have a number of pilot areas and no possibility of 
>>>random assignment.
>>>Therefore we have been given control areas.
>>>My problem is to select for survey individuals in the control areas who 
>>>match as closely as possible the randomly selected sample of action area 
>>>participants.
>>>As I understand the methodology, the procedure is to run a logistic 
>>>regression to determine the odds of a case being in the sample, across 
>>>both action and control areas, and then choose for control sample the 
>>>control area individual whose odds of being in the sample are closest to 
>>>an actual sample member.
>>>
>>>So far, I have following the multinomial logistic regression example in 
>>>Fox's Companion to Applied Regression.
>>>Firstly, I would like to know if the predict() is producing odds ratios 
>>>(or probabilities) for being in the sample, which is what I am aiming for.
>>
>>You asked for `probs', so you got probabilities.
>>
>>
>>
>>>Secondly, how do I get rownames (my unique identifier) into the output 
>>>from predict() - my input may be faulty somehow and the wrong rownames 
>>>being picked up - as I need to export back to database to sort and match 
>>>in names, addresses and phone numbers for my selected samples.
>>>
>>>My code is as follows:
>>>londonpsm <- sqlFetch(channel, "London_NW_london_pilots_elig", 
>>>rownames=ORCID)
>>>attach(londonpsm)
>>>mod.multinom <- multinom(sample ~ AGE + DISABLED + GENDER + ETHCODE + 
>>>NDYPTOT + NDLTUTOT + LOPTYPE)
>>>lonoutput <- predict(mod.multinom, sample, type='probs')
>>>london2 <- data.frame(lonoutput)
>>>
>>>The Logistic regression seems to work, although summary() says the it is 
>>>not a matrix.
>>>
>>
>>what is `it'?
>>
>>
>>
>>>The output looks like odds ratios, but I would like to know whether this 
>>>is so.
>>>
>>
>>No.
>>
>>
>
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-----------------------------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario, Canada L8S 4M4
email: jfox at mcmaster.ca
phone: 905-525-9140x23604
web: www.socsci.mcmaster.ca/jfox