Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"Sandra Virgo" <[email protected]> |

To |
<[email protected]> |

Subject |
st: Re: interpreting marginal effects of fractional logit with continuous independent variables |

Date |
Tue, 19 Nov 2013 12:10:42 +0000 |

To Richard Williams: Thanks very much for your response - I'm familiar with your Margins01 and Margins02 documents, but I'm grateful you've sent me the Margins03 document as I think that will be very helpful for me - the marginscontplot command by Royston could be just what I'm after. I will also look at discrete changes in the continuous variables (e.g. standard deviation changes) as you suggest. And I will see if I can get the Long and Freese book. I think that from what Austin has said I now have a way of interpreting the margins output with my specific IV - it is perhaps the scaling of that, rather than the fractional logit element that has made interpretation so difficult. Many thanks. To David Hoaglin: Hello David - thanks so much for your help. I get what you mean about all cases not having equal weight as denominators and denominators might vary a lot. I think I understand what you mean about the 'region of predictor space' - presumably you're asking how much of the variation in ple and llti_stand is actually present when I hold covariates at their means? I am happy to ignore the 'all else held at means' assumption, and therefore I won't try to calculate the Marginal Effects at the Mean as I also know the limitations of this. However, I'd still be interested in calculating Average Marginal Effects (i.e. with covariates taking on the actual values they have in my data) in the way that Richard Williams describes. And I'm guessing that it would still be OK from what you say to calculate predicted probabilities (fitted values/adjusted predictions to use other terminology) for specific scenarios. I would also like to calculate marginal effects at representative values (i.e. with covariates at their existing values apart from any I choose to fix at a series of values in order to explore 'interactions') as well as exploring the effects of discrete changes in my continuous variable. Hopefully these analyses will be OK from what you say. To Austin Nichols: Dear Austin - I'm very grateful for your help as it is either the scaling of my IVs of interest or the fact it is fractional logit that is making the output hard to interpret. Here's my output again: For the life expectancy variable the MEM: - ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] - -------------+---------------------------------------------------------------- ple | .0018984 .0007678 2.47 0.013 .0003935 .0034032 - ------------------------------------------------------------------------------ And for the illness prevalence variable the MEM: - ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] - -------------+---------------------------------------------------------------- llti_stand | -.5630636 .0485536 -11.60 0.000 -.658227 -.4679002 - ------------------------------------------------------------------------------ Does what you say mean for the interpretation of my llti_stand output that: "For every one percentage-point increase in llti_stand (age-standardised long-term limiting illness prevalence), the percentage of conceptions ending in maternity decreases by 56 hundredths of a percentage point (i.e. decreases by just over half a percentage point)? This is substantively much more likely as it's more similar to the result for life expectancy. Should my interpretation of the life expectancy (ple) result be the same as before: "for every one-year increase in life expectancy, the proportion of conceptions ending in maternity increases by .18, with all else held at means" ? As I have mentioned to David Hoaglin and Richard Williams, I'm happy not to calculate marginal effects at the mean due to the other problems with assumptions. But I'd still like to calculate Average Marginal Effects and Marginal Effects at representative values as well as looking at effects of discrete changes in the continuous variable, so being able to interpret the output for my specific independent variables in the way I think you have described is most helpful to me. Thanks everyone Sandra Sandra Virgo PhD Researcher Department of Population Health London School of Hygiene & Tropical Medicine 0207 299 4681 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:

- Prev by Date:
**Re: st: spearman rank correlation** - Next by Date:
**Re: st: Transformation of variable with pos/neg values via asinh** - Previous by thread:
**st: spearman rank correlation** - Next by thread:
**Re: st: Re: interpreting marginal effects of fractional logit with continuous independent variables** - Index(es):