R help - Jun 2003 - Logistic regression problem: propensity score matching

Hello all.

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. 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.
The output looks like odds ratios, but I would like to know whether this 
is so.

Thank you
Paul Bivand

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.

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595