<p>The following is my R code. I do not understand why for the same variable I have two estimated coefficient. <strong>How do I interpret Wife_age variable especially after exponentiate them?</strong> I believe it requires some transformation.</p>

<pre><code>mlogit.mod &lt;- mlogit(Contraceptive_method_used ~ 1|Wife_age  + Wife_education， data=mydata)
</code></pre>

<h1>Those are the stats, estimate, standard error, t- stats, p-value in that order</h1>

<pre><code>Variable  
2:Wife_age                     -0.045815,   0.012060, -3.7988, 0.0001454 

3:Wife_age                     -0.105979,   0.011256 -9.4150 &lt; 2.2e-16

2:Wife_education                0.881807,   0.114534,  7.6991, 1.377e-14 

3:Wife_education                0.366726,   0.087486,  4.1918 2.767e-05
</code></pre>

<h2>Confusion! This command will give me the odd ratio</h2>

<pre><code>exp(coef(mlogit.mod))

&gt; 2:Wife_age   **0.95521852**
</code></pre>

CC BY-SA 3.0
2018-04-17T18:47:39.150Z
This is multinomial logit. If you have 3 potential values for your dependent variable, the 1st one is used as a base case and thus omitted, and the 2 and 3 refer to the coefficients for outcome 2 and 3 respectively in relation to 1.
hipHopMetropolisHastings
CC BY-SA 3.0
2018-04-17T20:39:31.413Z
OK. the base case is model1. Then what does this base case represents in this situation?  How do I interpret the coefficients? Do I exponentiate the estimates?
Tom
CC BY-SA 3.0
2018-04-17T22:46:15.960Z
I mean the sign on the coefficient tells you if it increases or decreases the likelihood, but its tricky to interpret more than that. You have to use the logistic function to calculating the likelihood of each of your mutually exclusive categories being observed. I'd look at the wikipedia on multinomial logit if I were you.
hipHopMetropolisHastings
<econometrics>
Interpretation of R result, the mlogit
Tom
# Interpretation of R result, the mlogit

(Asked by: username_0 on 2018-04-15)

The following is my R code. I do not understand why for the same variable I have two estimated coefficient. How do I interpret Wife_age variable especially after exponentiate them? I believe it requires some transformation.
mlogit.mod <- mlogit(Contraceptive_method_used ~ 1|Wife_age  + Wife_education， data=mydata)

Those are the stats, estimate, standard error, t- stats, p-value in that order
Variable  
2:Wife_age                     -0.045815,   0.012060, -3.7988, 0.0001454 

3:Wife_age                     -0.105979,   0.011256 -9.4150 < 2.2e-16

2:Wife_education                0.881807,   0.114534,  7.6991, 1.377e-14 

3:Wife_education                0.366726,   0.087486,  4.1918 2.767e-05

Confusion! This command will give me the odd ratio
exp(coef(mlogit.mod))

> 2:Wife_age   **0.95521852**

**Tags:** econometrics
> **Comments:**
> **username_1**: This is multinomial logit. If you have 3 potential values for your dependent variable, the 1st one is used as a base case and thus omitted, and the 2 and 3 refer to the coefficients for outcome 2 and 3 respectively in relation to 1.  
> **username_0**: OK. the base case is model1. Then what does this base case represents in this situation?  How do I interpret the coefficients? Do I exponentiate the estimates?  
> **username_1**: I mean the sign on the coefficient tells you if it increases or decreases the likelihood, but its tricky to interpret more than that. You have to use the logistic function to calculating the likelihood of each of your mutually exclusive categories being observed. I'd look at the wikipedia on multinomial logit if I were you.  

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## 0 answers

