gamEst {PracTools} | R Documentation |
Estimate variance model parameter \gamma
Description
Regresses a y on a set of covariates X where Var_M(y)=\sigma^2x^\gamma
and then
regresses the squared residuals on log(x)
to estimate \gamma
.
Usage
gamEst(X1, x1, y1, v1)
Arguments
X1 |
matrix of predictors in the linear model for |
x1 |
vector of x's for individual units in the assumed specification of |
y1 |
vector of dependent variables for individual units |
v1 |
vector proportional to |
Details
The function gamEst
estimates the power \gamma
in a model where the variance
of the errors is proportional to x^\gamma
for some covariate x.
Values of \gamma
are typically in [0,2]. The function is iteratively called by gammaFit
, which is normally the function that an analyst should use.
Value
The estimate of \gamma
.
Author(s)
Richard Valliant, Jill A. Dever, Frauke Kreuter
References
Valliant, R., Dever, J., Kreuter, F. (2018, chap. 3). Practical Tools for Designing and Weighting Survey Samples, 2nd edition. New York: Springer.
See Also
Examples
data(hospital)
x <- hospital$x
y <- hospital$y
X <- cbind(sqrt(x), x)
gamEst(X1 = X, x1 = x, y1 = y, v1 = x)