Iteratively estimate variance model parameter γ
Description
Iteratively computes estimate of γ in a model with E_M(y)=x^Tβ and Var_M(y)=σ^2x^γ.
Usage
1 
Arguments
X 
matrix of predictors in the linear model for y 
x 
vector of x's for individual units in the assumed specification of Var_M(y) 
y 
vector of dependent variables for individual units 
maxiter 
maximum number of iterations allowed 
show.iter 
should values of γ be printed of each iteration? 
tol 
size of relative difference in \hat{γ}'s between consecutive iterations
used to determine convergence. Algorithm terminates when relative difference
is less than 
Details
The function gammaFit
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 calls gamEst
.
Value
A list with the components:
g.hat 
estimate of γ when iterative procedure stopped 
converged 

steps 
number of steps used by the algorithm 
Author(s)
Richard Valliant, Jill A. Dever, Frauke Kreuter
References
Valliant, R., Dever, J., Kreuter, F. (2013, chap. 3). Practical Tools for Designing and Weighting Survey Samples. New York: Springer.
See Also
gamEst
Examples
1 2 3 4 5 6 