so, what did you expect instead as a result of an orderd logistic
regression?
See http://www.ats.ucla.edu/stat/R/dae/ologit.htm for interpretational help.
hth.
Mathew, Abraham T schrieb:
I ran the follow code for an ordered logit, but don't know why two levels of my
dependent variable are at the topic of my list of variables.
I don't know why this appears, and what I'm supposed to take from them
y>=0. Haven't thought much about this
y>=1. Favor
library(Design)
two <- lrm(trade1 ~ age2 + education2 + personal2 + economy2 + partisan2 +
employment2 + union2 + home2 + market2 + race2 + income2)
two
summary(two)
#Logistic Regression Model
#
#lrm(formula = trade1 ~ age2 + education2 + personal2 + economy2 +
# partisan2 + employment2 + union2 + home2 + market2 + race2 +
# income2)
#
#Frequencies of Responses
# 5. Oppose 0. Haven't thought much about this
# 258 311
# 1. Favor
# 209
#
#Frequencies of Missing Values Due to Each Variable
# trade1 age2 education2 personal2 economy2 partisan2
# 210 0 3 7 16 134
#employment2 union2 home2 market2 race2 income2
# 678 5 59 207 10 82
#
# Obs Max Deriv Model L.R. d.f. P C Dxy
# 778 1e-10 79.47 11 0 0.647 0.295
# Gamma Tau-a R2 Brier
# 0.296 0.194 0.11 0.191
#
# Coef S.E. Wald Z P
#y>=0. Haven't thought much about this 3.045016 0.879724 3.46 0.0005
#y>=1. Favor 1.198983 0.873244 1.37 0.1697
#age2 0.003499 0.006006 0.58 0.5602
#education2 -0.232741 0.048080 -4.84 0.0000
#personal2 -0.117132 0.089053 -1.32 0.1884
#economy2 -0.308168 0.104512 -2.95 0.0032
#partisan2 -0.103308 0.091803 -1.13 0.2605
#employment2 -0.097818 0.378070 -0.26 0.7958
#union2 0.038079 0.168730 0.23 0.8215
#home2 0.274581 0.157926 1.74 0.0821
#market2 -0.195350 0.153563 -1.27 0.2033
#race2 -0.057408 0.112952 -0.51 0.6113
#income2 -0.130017 0.068048 -1.91 0.0560
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Eik Vettorazzi
Institut für Medizinische Biometrie und Epidemiologie
Universitätsklinikum Hamburg-Eppendorf
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