Estimate variance model parameter γ
Regresses a y on a set of covariates X where Var_M(y)=σ^2x^γ and then regresses the squared residuals on log(x) to estimate γ.
gamEst(X1, x1, y1, v1)
X1 |
matrix of predictors in the linear model for |
x1 |
vector of x's for individual units in the assumed specification of Var_M(y) |
y1 |
vector of dependent variables for individual units |
v1 |
vector proportional to Var_M(y) |
The function gamEst estimates the power γ in a model where the variance
of the errors is proportional to x^γ for some covariate x.
Values of γ are typically in [0,2]. The function is iteratively called by gammaFit, which is normally the function that an analyst should use.
The estimate of γ.
Richard Valliant, Jill A. Dever, Frauke Kreuter
Valliant, R., Dever, J., Kreuter, F. (2013, chap. 3). Practical Tools for Designing and Weighting Survey Samples. New York: Springer.
data(hospital) x <- hospital$x y <- hospital$y X <- cbind(sqrt(x), x) gamEst(X1 = X, x1 = x, y1 = y, v1 = x)
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