search for: pscore

Hello , My optmatch package is loaded on R and otherwise running fine. I get an error after lcds successfully completes a logistic regression and I then try to obtain a propensity score: pdist <- pscore.dist(lcds) Error: could not find function "pscore.dist" Does anyone know if pscore.dist is part of the optmatch package, or is it contained in another package that I need to load? I searched the help files, other online sources, could find no answer for this. Any advice would be gre...

Hello My optmatch package is loaded and otherwise running fine. I get an error after lcds successfully completes logistic regression and I'm trying to obtain a propensity score: > pdist <- pscore.dist(lcds) Error: could not find function "pscore.dist" I searched the help files, other online sources, could find no answer for this. Any advice would be greatly appreciated! Thank you Michael Adolph

...s = km$cluster, color = col.vec[km$clusters]) Cheers Matt, that worked great. Secondly I wanted to know how to plot just the loadings of a PCA as biplot arrows, i.e. plot a biplot but without plotting the labels for the scores. I solved this by taking the biplot.default code and adding an option pscores to the call. This is FALSE by default. I then put an if clause before the bit of the code that plots the labels for the PCA scores, so that they only get plotted if you specify pscores=TRUE. My function for doing this is copied below, with a couple of extra modifications so that I can get the p...

...zelig package provides an alternative. My logit model is as simple at its gets: Y~X, where Y is a treament vector and X is matrix of some covariates. I wonder right now if te glm respectively summary(glm(...)) puts out something comparable to ML estimates that can be used as the estimated pscores, in such a way that there is one value for every observation. Thanks for any help in advance

...a problem on extracting T-Stat and P-Value. I have written R-code below library("Matching") data("lalonde") attach(lalonde) names(lalonde) Y <- lalonde$re78 Tr <- lalonde$treat glm1 <- glm(Tr~age+educ+black+hisp+married+nodegr+re74+re75,family=binomial,data=lalonde) pscore.predicted <- predict(glm1) rr1 <- Match(Y=Y,Tr=Tr,X=glm1$fitted,estimand="ATT", M=1,ties=TRUE,replace=TRUE) summary(rr1) > summary(rr1) Estimate...  2624.3  AI SE......  802.19  T-stat.....  3.2714  p.val......  0.0010702  Original number of observations..............  445 ...