secondDiff.Rd
Calculates the cross-derivative required to evaluate interactions in logistic/probit regression models.
secondDiff( obj, vars, data, method = c("AME", "MER"), vals = NULL, typical = NULL )
obj | An object of class |
---|---|
vars | A vector of two variables to be used in calculating the derivative. |
data | A data frame. |
method | Indicate whether you want to use average marginal effects (AME) or marginal effects at representative values (MER). |
vals | A named list of length 2 where each element gives the minimum and maximum values used in the calculation. |
typical | A named vector of values at which to hold variables constant. |
A list with two elements:
The average second difference in each iteration of the bootstrap.
If type == 'AME'
, ind
is returned with
the second difference and measures of uncertainty for each individual
observation in the original dataset
If type == 'MER'
,
probs
is returned with the full matrix of simulated predicted
probabilities for the four conditions.
The function calculates the second difference as (Pr(Y=1|x1=max, x2=max) - Pr(Y=1|x1=min, x2=max)) - (Pr(Y=1|x1=max, x2=min) - Pr(Y=1|x1=min, x2=min)). The function uses a parametric bootstrap to calculate the sampling distribution of the second difference.