Marginal effects for a probit regression.
This function estimates a probit regression model and calculates the corresponding marginal effects.
probitmfx(formula, data, atmean = TRUE, robust = FALSE, clustervar1 = NULL,
clustervar2 = NULL, start = NULL, control = list())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 |
robust |
if |
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 |
control |
see |
If both robust=TRUE and !is.null(clustervar1) the function overrides the robust
command and computes clustered standard errors.
mfxest |
a coefficient matrix with columns containing the estimates, associated standard errors, test statistics and p-values. |
fit |
the fitted |
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. |
William H. Greene (2008). Econometric Analysis (6th ed.). Prentice Hall, N.Y. pp 770-787.
# 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) probitmfx(formula=y~x, data=data)
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