Description Usage Arguments Details Value References See Also Examples

Computes a Wald *chi-squared* test for 1 or more coefficients, given their variance-covariance matrix.

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`Sigma` |
A var-cov matrix, usually extracted from one of the fitting functions (e.g., |

`b` |
A vector of coefficients with var-cov matrix |

`Terms` |
An optional integer vector specifying which coefficients should be |

`L` |
An optional matrix conformable to |

`H0` |
A numeric vector giving the null hypothesis for the test. It must be as long as |

`df` |
A numeric vector giving the degrees of freedom to be used in an |

`verbose` |
A logical scalar controlling the amount of output information. The default is |

`x` |
Object of class “wald.test” |

`digits` |
Number of decimal places for displaying test results. Default to 2. |

`...` |
Additional arguments to |

The key assumption is that the coefficients asymptotically follow a (multivariate) normal distribution with mean =
model coefficients and variance = their var-cov matrix.

One (and only one) of `Terms`

or `L`

must be given. When `L`

is given, it must have the same number of
columns as the length of `b`

, and the same number of rows as the number of linear combinations of coefficients.
When `df`

is given, the *chi-squared* Wald statistic is divided by `m`

= the number of
linear combinations of coefficients to be tested (i.e., `length(Terms)`

or `nrow(L)`

). Under the null
hypothesis `H0`

, this new statistic follows an *F(m, df)* distribution.

An object of class `wald.test`

, printed with `print.wald.test`

.

Diggle, P.J., Liang, K.-Y., Zeger, S.L., 1994. Analysis of longitudinal data. Oxford, Clarendon Press, 253 p.

Draper, N.R., Smith, H., 1998. Applied Regression Analysis. New York, John Wiley & Sons, Inc., 706 p.

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