gamEst | R Documentation |

`\gamma`

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`

.

```
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 `\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.

The estimate of `\gamma`

.

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)
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.