Dear All:
I run a logistic regression (using lrm in the Design package), and
after that, I use the command "summary" to get the marginal effects
of each variable. But one strange thing happens on my binary
dependent variable: The marginal effect of it jumping from 0 to 1 is
1.77. I believe the marginal effect of binary variable x1 has
interpretation should be P(Y=1|x1=1, x2...)-P(Y=1|x1=0,x2...). As
both terms lies in [0,1], their difference shouldn't be larger than 1.
Besides this, I also get some boundary number for the marginal effect
of the same binary variable (in datasets of other years)like .98, .
97, with which I am not comfortable either. I suspect I did something
wrong.
This is part of my model:
> resultt1
Logistic Regression Model
lrm(formula = typemort ~ adv_binc_ratio + agem1 + regEA + regEM +
regGL + regN + regNI + regNW + regS + regSW + regW + regWM +
regY + repmethIO + repmethSR + no_dis_no_def + prevLO + prevOO +
prevRP + owning + adv_binc_ratio * (repmethIO + repmethSR +
no_dis_no_def + prevLO + prevOO + prevRP + owning) + agem1 *
(repmethIO + repmethSR + no_dis_no_def + prevLO + prevOO +
prevRP + owning), data = a)
This is part of my result:
> summary(resultt1,adv_binc_ratio=mean(a$adv_binc_ratio),agem1=mean(a
$agem1),repmethIO=c(0,mean(a$repmethIO),1),repmethSR=c(0,mean(a
$repmethSR),1),no_dis_no_def=c(0,mean(a$no_dis_no_def),1),prevLO=c
(0,mean(a$prevLO),1),prevOO=c(0,mean(a$prevOO),1),prevRP=c(0,mean(a
$prevRP),1),regEA=c(0,mean(a$regEA),1),regEM=mean(a$regEM),regGL=mean
(a$regGL),regN=mean(a$regN),regNI=mean(a$regNI),regNW=mean(a
$regNW),regS=mean(a$regS),regSW=mean(a$regSW),regW=mean(a
$regW),regWM=mean(a$regWM),regY=mean(a$regY),owning=c(0,mean(a
$owning),1))
Effects Response : typemort
Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
no_dis_no_def 0.0000 1.0000 1.0000 1.76 0.03 1.69 1.82
Odds Ratio 0.0000 1.0000 1.0000 5.79 NA 5.41 6.19
Adjusted to: adv_binc_ratio=2.611027 agem1=40.47638
repmethIO=0.1456293 repmethSR=0.6711471 no_dis_no_def=0.4463533
prevLO=0.06590113 prevOO=0.7785591 prevRP=0.06738472 owning=0.4765593
Thank you very much for your help.
Thanks,
Minyu Chen
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