logitmfx function

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)