Description Usage Arguments Details Value Author(s) References See Also Examples

`summary`

method for objects of class `"iwlsm"`

1 2 3 |

`object` |
the fitted model.
This is assumed to be the result of some fit that produces
an object inheriting from the class |

`method` |
Should the weighted (by the IWLS weights) or unweighted cross-products matrix be used? |

`correlation` |
logical. Should correlations be computed (and printed)? |

`...` |
arguments passed to or from other methods. |

This function is a method for the generic function
`summary()`

for class `"iwlsm"`

.
It can be invoked by calling `summary(x)`

for an
object `x`

of the appropriate class, or directly by
calling `summary.iwlsm(x)`

regardless of the
class of the object.

If printing takes place, only a null value is returned.
Otherwise, a list is returned with the following components.
Printing always takes place if this function is invoked automatically
as a method for the `summary`

function.

`correlation` |
The computed correlation coefficient matrix for the coefficients in the model. |

`cov.unscaled` |
The unscaled covariance matrix; i.e, a matrix such that multiplying it by an estimate of the error variance produces an estimated covariance matrix for the coefficients. |

`sigma` |
The scale estimate. |

`stddev` |
A scale estimate used for the standard errors. |

`df` |
The number of degrees of freedom for the model and for residuals. |

`coefficients` |
A matrix with three columns, containing the coefficients, their standard errors and the corresponding t statistic. |

`terms` |
The terms object used in fitting this model. |

Adapted by Ruth Ripley

Venables, W. N. and Ripley, B. D. (2002)
*Modern Applied Statistics with S.* Fourth edition. Springer.
See also http://www.stats.ox.ac.uk/~snijders/siena/

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
## Not run:
##not enough data here for a sensible example, but shows the idea.
myalgorithm <- sienaAlgorithmCreate(nsub=2, n3=100)
mynet1 <- sienaDependent(array(c(s501, s502), dim=c(50, 50, 2)))
mynet2 <- sienaDependent(array(c(s502, s503), dim=c(50, 50, 2)))
mydata1 <- sienaDataCreate(mynet1)
mydata2 <- sienaDataCreate(mynet2)
myeff1 <- getEffects(mydata1)
myeff2 <- getEffects(mydata2)
myeff1 <- setEffect(myeff1, transTrip, fix=TRUE, test=TRUE)
myeff2 <- setEffect(myeff2, transTrip, fix=TRUE, test=TRUE)
myeff1 <- setEffect(myeff1, cycle3, fix=TRUE, test=TRUE)
myeff2 <- setEffect(myeff2, cycle3, fix=TRUE, test=TRUE)
ans1 <- siena07(myalgorithm, data=mydata1, effects=myeff1, batch=TRUE)
ans2 <- siena07(myalgorithm, data=mydata2, effects=myeff2, batch=TRUE)
meta <- siena08(ans1, ans2)
metadf <- split(meta$thetadf, meta$thetadf$effects)[[1]]
metalm <- iwlsm(theta ~ tconv, metadf, ses=se^2)
summary(metalm)
## End(Not run)
``` |

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