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Estimate parameters in a SURE model.
sureEst(y, x, v, sigma, iter=10000, tol=1e-12)
a n by p matrix, whose columns are dependent variables.
a n by m matrix, whose columns are predictor variables to select from.
a list; v[[j]] indicates which x's in the model for y[,j].
initial residual variance-covarance matrix (if given).
maximum number of iterations in a numerical process to estimate model parameters.
convergence tolerance.
a list with the following components:
log-likelihood of the model
estimates of model coefficients
estimated residual variance-covariance
fitted mean values
# NOT RUN { data(etrait) x<- as.matrix(mdat-1/2) y<- as.matrix(traits)[,1:3] v<- list(c(1,25,50),numeric(0),3) # } # NOT RUN { o<- sureEst(y, x, v=v, iter=250, tol=1e-12) # }
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