Logistic regression problem: propensity score matching
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). The response variable is named sample because that is what it is - I am new to R so haven't quite got into habits of naming using Title Case. Having selected a sample from the action area, randomly, the aim is to find people to survey who, if they had been in the action area, would have had as close odds of being in the samle as those actually selected. Therefore I select a sample using sample(), write that out back to the access database as a new table, then (from R) run a query which creates a dataset comprising all those in the action area who could have been selected, and the control area group, and read this back in to R (using as many characteristics as possible except area) before undertaking the logistic regression. sample can take the values 0 (not in sample) or 1 (n sample). The aim is to find the odds of being in the sample (by characteristics) which is the Propensity Score, and then match action to control using Propensity Score Matching. I have MASS but was unable to locate logistic regression, which I was advised was the standard method for my problem. 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.