# gamEst: Estimate variance model parameter gamma In PracTools: Tools for Designing and Weighting Survey Samples

 gamEst R Documentation

## Estimate variance model parameter γ

### Description

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.

### Usage

```gamEst(X1, x1, y1, v1)
```

### Arguments

 `X1` matrix of predictors in the linear model for `y1` `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)

### Details

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.

### Value

The estimate of γ.

### 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.

`gammaFit`

### Examples

```data(hospital)
x <- hospital\$x
y <- hospital\$y

X <- cbind(sqrt(x), x)
gamEst(X1 = X, x1 = x, y1 = y, v1 = x)
```

PracTools documentation built on Aug. 17, 2022, 5:06 p.m.