# Posts Tagged ‘ Logit ’

## Probit/Logit Marginal Effects in R

April 23, 2012
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The common approach to estimating a binary dependent variable regression model is to use either the logit or probit model. Both are forms of generalized linear models (GLMs), which can be seen as modified linear regressions that allow the dependent variable to originate from non-normal distributions. The coefficients in a linear regression model are marginal

## Stata-like Marginal Effects for Logit and Probit Models in R [2]

May 18, 2011
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My thanks to those who emailed comments and suggestions for my ‘mfx’ function, I’m happy that I could fill a void for some people. I also received a request/suggestion from Tony Cookson, along with a helpful fix for a bug in the code, to include an option that would allow the user to specify values

## Stata-like Marginal Effects for Logit and Probit Models in R

May 17, 2011
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$Stata-like Marginal Effects for Logit and Probit Models in R$

Although this blog’s primary focus is time series, one feature I missed from Stata was the simple marginal effects command, ‘mfx compute’, for cross-sectional work, and I could not find an adequate replacement in R. To bridge this gap, I’ve written a (rather messy) R function to produce marginal effects readout for logit and probit