Excuse me Professor, actually for unordered multiple categories depend variable, there are two alternative models, which are multinomial logit (MNL) and multinomial probit (MNP). How to choose the suitable model ?
Hi, average marginal effect (AME) of an independent variable (IV) is a derivative that explains how probable an outcome is. In other words, it estimates the partial derivatives with respect to IV using all the observed values of other covariates, and then takes the average (result presented). In case of binary variables, it estimates the finite difference of between the effect of the specified group that IV has defined using the same process. As for interpretation, if the IV is sex that takes the value 1 when one is a man, then AME would show how much probable one falls in a category when being a man. There is no reference group since margins command as presented shows AMEs for all IVs. In fact, there was an easier way to estimate AMEs for all variables by typing: margins, dydx(*). One can estimate marginal effects with a base group by using *contrasts of margins* . Search about contrasts of margins and you will find how (I cannot provide many details because I haven't done it frequently).
Porque para rodar o modelo logit multinomial você deve excluir uma categoria base das variáveis categóricas. Ex: meu modelo a minha variável dependente tem quatro categorias (segurança alimentar, Insegurança alimentar leve, Insegurança alimentar moderada e insegurança alimentar grave) minha variável categórica é a segurança alimentar, e de todas as minhas variáveis explicativas, exclui como categoria base aquelas categorias que apresentavam maior percentual de segurança alimentar.
Thank you my friend. Greetiings from Brazil.
Hi! Could you provide an interpretation of AGE in the marginal effects table? Thx
Excuse me Professor, actually for unordered multiple categories depend variable, there are two alternative models, which are multinomial logit (MNL) and multinomial probit (MNP). How to choose the suitable model ?
it's very amazing video, how to interpret each variable in the different poverty levels according to the base?
Please explain the interpretation
Thank you but I have explained it already. Let me know what you don't understand?
It is very nice video. Kindly help as how to interprate
In marginal effect like parameter estimating, can we use base category.?
Hi, average marginal effect (AME) of an independent variable (IV) is a derivative that explains how probable an outcome is. In other words, it estimates the partial derivatives with respect to IV using all the observed values of other covariates, and then takes the average (result presented). In case of binary variables, it estimates the finite difference of between the effect of the specified group that IV has defined using the same process.
As for interpretation, if the IV is sex that takes the value 1 when one is a man, then AME would show how much probable one falls in a category when being a man. There is no reference group since margins command as presented shows AMEs for all IVs. In fact, there was an easier way to estimate AMEs for all variables by typing: margins, dydx(*).
One can estimate marginal effects with a base group by using *contrasts of margins* . Search about contrasts of margins and you will find how (I cannot provide many details because I haven't done it frequently).
How can a negative marginal effect education variable coefficient be interpreted be interpreted?
How can i export marginal effect table into word?
Could you explain the difference between nrep() and baseoutcome() ?
baseautcome is the base category
Porque para rodar o modelo logit multinomial você deve excluir uma categoria base das variáveis categóricas. Ex: meu modelo a minha variável dependente tem quatro categorias (segurança alimentar, Insegurança alimentar leve, Insegurança alimentar moderada e insegurança alimentar grave) minha variável categórica é a segurança alimentar, e de todas as minhas variáveis explicativas, exclui como categoria base aquelas categorias que apresentavam maior percentual de segurança alimentar.
Thank you so much!!
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