An MLwiN model run via the IGLS estimation method is represented by an "mlwinfitIGLS" object

`Nobs`

Computes the number of complete observations.

`DataLength`

Total number of cases.

`Hierarchy`

For each higher level of a multilevel model, returns the number of units at that level, together with the minimum, mean and maximum number of lower-level units nested within units of the current level.

`D`

A vector specifying the type of distribution to be modelled, which can include

`'Normal'`

,`'Binomial'`

`'Poisson'`

,`'Multinomial'`

,`'Multivariate Normal'`

, or`'Mixed'`

.`Formula`

A formula object (or a character string) specifying a multilevel model.

`levID`

A character string (vector) of the specified level ID(s).

`contrasts`

A list of contrast matrices, one for each factor in the model.

`xlevels`

A list of levels for the factors in the model.

`FP`

Displays the fixed part estimates.

`RP`

Displays the random part estimates.

`FP.cov`

Displays a covariance matrix of the fixed part estimates.

`RP.cov`

Displays a covariance matrix of the random part estimates.

`elapsed.time`

Calculates the CPU time used for fitting the model.

`call`

The matched call.

`LIKE`

The deviance statistic (-2*log(like)).

`Converged`

Boolean indicating whether the model has converged

`Iterations`

Number of iterations that the model has run for

`Meth`

If

`Meth = 0`

estimation method is set to RIGLS. If`Meth = 1`

estimation method is set to IGLS.`residual`

If

`resi.store`

is`TRUE`

, then the residual estimates at all levels are returned.`data`

The data.frame that was used to fit the model.

`nonlinear`

A character vector specifying linearisation method used. The first element specifies marginal quasi-likelihood linearization (

`N = 0`

) or penalised quasi-likelihood linearization (`N = 1`

); The second element specifies first (`M = 1`

) or second (`M = 2`

) order approximation.`version`

The MLwiN version used to fit the model

An instance is created by calling function `runMLwiN`

.

Zhang, Z., Charlton, C.M.J., Parker, R.M.A., Leckie, G., and Browne, W.J. (2016) Centre for Multilevel Modelling, University of Bristol.

`runMLwiN`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## Not run:
library(R2MLwiN)
# NOTE: if MLwiN not saved in location R2MLwiN defaults to, specify path via:
# options(MLwiN_path = 'path/to/MLwiN vX.XX/')
# If using R2MLwiN via WINE, the path may look like this:
# options(MLwiN_path = '/home/USERNAME/.wine/drive_c/Program Files (x86)/MLwiN vX.XX/')
## Example: tutorial
data(tutorial, package = "R2MLwiN")
(mymodel <- runMLwiN(normexam ~ 1 + standlrt + (1 + standlrt | school) + (1 | student),
data = tutorial))
##summary method
summary(mymodel)
##logLik method
logLik(mymodel)
## End(Not run)
``` |

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