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, [EMAIL PROTECTED] 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 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
