Dear Paul, I tried polr() and lrm() on a different problem and (except for the difference in signs for the cut-points/intercepts) got identical results for both coefficients and standard errors. There might be something ill-conditioned about your problem that produces the discrepancy -- I noticed, for example, that some of the upper categories of the response are very sparse. Perhaps the two functions use different forms of the information matrix. I expect that someone else will be able to supply more details.
I believe that the t-statistics in the polr() output are actually Wald statistics. I hope this helps, John > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Paul Johnson > Sent: Thursday, September 30, 2004 4:41 PM > To: r help > Subject: [R] polr (MASS) and lrm (Design) differences in > tests of statistical signifcance > > Greetings: > > I'm running R-1.9.1 on Fedora Core 2 Linux. > > I tested a proportional odds logistic regression with MASS's > polr and Design's lrm. Parameter estimates between the 2 are > consistent, but the standard errors are quite different, and > the conclusions from the t and Wald tests are dramatically > different. I cranked the "abstol" argument up quite a bit in > the polr method and it did not make the differences go away. > > So > > 1. Can you help me see why the std. errors in the polr are so > much smaller, and > > 2. Can I hear more opinions on the question of t vs. Wald in > making these signif tests. So far, I understand the t is > based on the asymptotic Normality of the estimate of b, and > for finite samples b/se is not exactly distributed as a t. > But I also had the impression that the Wald value was an > approximation as well. > > > summary(polr(as.factor(RENUCYC) ~ DOCS + PCT65PLS*RANNEY2 > + OLDCRASH > + FISCAL2 + PCTMETRO + ADMLICEN, data=elaine1)) > > Re-fitting to get Hessian > > Call: > polr(formula = as.factor(RENUCYC) ~ DOCS + PCT65PLS * RANNEY2 + > OLDCRASH + FISCAL2 + PCTMETRO + ADMLICEN, data = elaine1) > > Coefficients: > Value Std. Error t value > DOCS 0.004942217 0.002952001 1.674192 > PCT65PLS 0.454638558 0.113504288 4.005475 > RANNEY2 0.110473483 0.010829826 10.200855 > OLDCRASH 0.139808663 0.042245692 3.309418 > FISCAL2 0.025592117 0.011465812 2.232037 > PCTMETRO 0.018184093 0.007792680 2.333484 > ADMLICEN -0.028490387 0.011470999 -2.483688 > PCT65PLS:RANNEY2 -0.008559228 0.001456543 -5.876400 > > Intercepts: > Value Std. Error t value > 2|3 6.6177 0.3019 21.9216 > 3|4 7.1524 0.2773 25.7938 > 4|5 10.5856 0.2149 49.2691 > 5|6 12.2132 0.1858 65.7424 > 6|8 12.2704 0.1856 66.1063 > 8|10 13.0345 0.2184 59.6707 > 10|12 13.9801 0.3517 39.7519 > 12|18 14.6806 0.5587 26.2782 > > Residual Deviance: 587.0995 > AIC: 619.0995 > > > > lrm(RENUCYC ~ DOCS + PCT65PLS*RANNEY2 + OLDCRASH + > FISCAL2 + PCTMETRO + ADMLICEN, data=elaine1) > > Logistic Regression Model > > lrm(formula = RENUCYC ~ DOCS + PCT65PLS * RANNEY2 + OLDCRASH + > FISCAL2 + PCTMETRO + ADMLICEN, data = elaine1) > > > Frequencies of Responses > 2 3 4 5 6 8 10 12 18 > 21 12 149 46 1 10 6 2 2 > > Frequencies of Missing Values Due to Each Variable > RENUCYC DOCS PCT65PLS RANNEY2 OLDCRASH FISCAL2 > PCTMETRO ADMLICEN > 5 0 0 6 0 5 > 0 5 > > Obs Max Deriv Model L.R. d.f. P C > Dxy > 249 7e-05 56.58 8 0 0.733 > 0.465 > Gamma Tau-a R2 Brier > 0.47 0.278 0.22 0.073 > > Coef S.E. Wald Z P > y>=3 -6.617857 6.716688 -0.99 0.3245 > y>=4 -7.152561 6.716571 -1.06 0.2869 > y>=5 -10.585705 6.742222 -1.57 0.1164 > y>=6 -12.213340 6.755656 -1.81 0.0706 > y>=8 -12.270506 6.755571 -1.82 0.0693 > y>=10 -13.034584 6.756829 -1.93 0.0537 > y>=12 -13.980235 6.767724 -2.07 0.0389 > y>=18 -14.680760 6.786639 -2.16 0.0305 > DOCS 0.004942 0.002932 1.69 0.0918 > PCT65PLS 0.454653 0.552430 0.82 0.4105 > RANNEY2 0.110475 0.076438 1.45 0.1484 > OLDCRASH 0.139805 0.042104 3.32 0.0009 > FISCAL2 0.025592 0.011374 2.25 0.0245 > PCTMETRO 0.018184 0.007823 2.32 0.0201 > ADMLICEN -0.028490 0.011576 -2.46 0.0138 > PCT65PLS * RANNEY2 -0.008559 0.006417 -1.33 0.1822 > > > > > -- > Paul E. Johnson email: [EMAIL PROTECTED] > Dept. of Political Science http://lark.cc.ku.edu/~pauljohn > 1541 Lilac Lane, Rm 504 > University of Kansas Office: (785) 864-9086 > Lawrence, Kansas 66044-3177 FAX: (785) 864-5700 > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html