gamEst | R Documentation |

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 |

`y1` |
vector of dependent variables for individual units |

`v1` |
vector proportional to |

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. (2018, chap. 3). *Practical Tools for Designing and Weighting Survey Samples, 2nd edition*. New York: Springer.

`gammaFit`

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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