evalGmm-methods {momentfit} | R Documentation |
evalGmm
in Package modelfit ~~Method to simply evaluate a GMM model at a fixed coefficient vector. It
creates a "gmmfit"
object using that fixed vector.
## S4 method for signature 'momentModel' evalGmm(model, theta, wObj=NULL, ...) ## S4 method for signature 'sysModel' evalGmm(model, theta, wObj=NULL, ...)
model |
An object of class |
theta |
A vector of coefficients at which the model is estimated |
wObj |
An object of class |
... |
Other arguments to pass. Not used for the moment. |
signature(model = "momentModel")
signature(model = "sysModel")
data(simData) theta <- c(beta0=1,beta1=2) ## A linear model model1 <- momentModel(y~x1, ~z1+z2, data=simData) evalGmm(model1, c(1,1)) ## A nonlinear model g <- y~beta0+x1^beta1 h <- ~z1+z2 model2 <- momentModel(g, h, c(beta0=1, beta1=2), data=simData) evalGmm(model2, theta=c(beta1=2, beta0=0.5)) ## A function model fct <- function(tet, x) { m1 <- (tet[1] - x) m2 <- (tet[2]^2 - (x - tet[1])^2) m3 <- x^3 - tet[1]*(tet[1]^2 + 3*tet[2]^2) f <- cbind(m1, m2, m3) return(f) } dfct <- function(tet, x) { jacobian <- matrix(c( 1, 2*(-tet[1]+mean(x)), -3*tet[1]^2-3*tet[2]^2,0, 2*tet[2], -6*tet[1]*tet[2]), nrow=3,ncol=2) return(jacobian) } model3 <- momentModel(fct, simData$x3, theta0=c(beta0=1, beta1=2), grad=dfct) evalGmm(model3, theta=c(beta1=.1, beta0=0.3))