logitmfx function

Marginal effects for a logit regression.

Marginal effects for a logit regression.

This function estimates a binary logistic regression model and calculates the corresponding marginal effects.

logitmfx(formula, data, atmean = TRUE, robust = FALSE, clustervar1 = NULL, clustervar2 = NULL, start = NULL, control = list())

Arguments

  • formula: an object of class ``formula'' (or one that can be coerced to that class).
  • data: the data frame containing these data. This argument must be used.
  • atmean: default marginal effects represent the partial effects for the average observation. If atmean = FALSE the function calculates average partial effects.
  • robust: if TRUE the function reports White/robust standard errors.
  • clustervar1: a character value naming the first cluster on which to adjust the standard errors.
  • clustervar2: a character value naming the second cluster on which to adjust the standard errors for two-way clustering.
  • start: starting values for the parameters in the glm model.
  • control: see glm.control.

Details

If both robust=TRUE and !is.null(clustervar1) the function overrides the robust

command and computes clustered standard errors.

Returns

  • mfxest: a coefficient matrix with columns containing the estimates, associated standard errors, test statistics and p-values.

  • fit: the fitted glm object.

  • dcvar: a character vector containing the variable names where the marginal effect refers to the impact of a discrete change on the outcome. For example, a factor variable.

  • call: the matched call.

References

William H. Greene (2008). Econometric Analysis (6th ed.). Prentice Hall, N.Y. pp 770-787.

See Also

logitor, glm

Examples

# simulate some data set.seed(12345) n = 1000 x = rnorm(n) # binary outcome y = ifelse(pnorm(1 + 0.5*x + rnorm(n))>0.5, 1, 0) data = data.frame(y,x) logitmfx(formula=y~x, data=data)
  • Maintainer: Alan Fernihough
  • License: GPL-2 | GPL-3
  • Last published: 2019-02-06

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