grad1: Gradient of bivariate probit with partial observability
In BiProbitPartial: Bivariate Probit with Partial Observability

Description Usage Arguments Value

Gradient of bivariate probit with partial observability

1	grad1(theta, X1, X2, Z, rho = 0, p = NULL, summed = T, fixrho = F)

`theta`	numeric vector of dimension equal to that of the free parameter space
`X1`	numeric matrix of covariates for the first equation
`X2`	numeric matrix of covariates for the second equation
`Z`	numeric matrix or column vecotr of response observations
`rho`	numeric value for rho if fixed
`p`	numeric precomputed probabilities of Pr(Y1=1,Y2=1)
`summed`	logical if the gradient observations should be summed
`fixrho`	logical if rho should be fixed

if summed is TRUE then the function returns the numeric column sum of the gradient matrix, else it returns a numeric vector with each entry a value of the gradient vector

BiProbitPartial documentation built on May 2, 2019, 1:48 p.m.