[R] Marginal Effect larger than 1 for a binary variable (summary.Design after lrm)

From: Minyu Chen <minyu.chen_at_ucl.ac.uk>
Date: Fri 27 Oct 2006 - 16:54:22 GMT

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:

\$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

```

Thank you very much for your help.

Thanks,
Minyu Chen

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