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 -5.876400Intercepts:
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 26.2782Residual 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 5Obs 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
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