Library: | eiv |
See also: | eivvec1 |
Quantlet: | eivvec2 | |
Description: | eivvect2 calculates the maximum likelihood estimators of the parameters in the measurement error models when the entire error covariance structure is known or known up to a scalar multiple. This quantlet deals with the extension of the model considered in eivknownvaru. |
Usage: | {hatbeta,sigmam)=eivvec2(w,y,Gamma) | |
Input: | ||
w | n x p matrix, the design variables | |
y | n x 1 matrix, the response | |
Gamma | (p+1) x (p+1) matrix | |
Output: | ||
hatbeta | vector, the estimate | |
sigmam | scalar, the estimate |
library("xplore") library("eiv") y=#(1.015, 1.120, 1.937, 1.743, 2.173) w=0*matrix(5,3) w[,1]=#(1,1,1,1) w[,2]=#(3.442, 3.180,3.943, 3.983, 4.068) w[,3]=#(0.477, 0.610, 0.505, 0.415, 0.620) sig=0*matrix(4,4) sig[,1]=#(0.5546, 0,-0.1079, -0.0691) sig[,2]=#(0,0,0,0) sig[,3]=#(-0.1079,0,0.2756,0.1247) sig[,4]=#(-0.0691,0,0.1247,0.0878) Gamma=0.01*sig gest=eivvec2(w,y,Gamma) gest.hatbeta gest.sigmam
Contents of hatbeta [1,] -2.7727e+13 [2,] 0.24253 [3,] 0 Contents of sigmam [1,] 0.030575