An S4 class that stores the outputs of the fitted IGLS model.

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Description

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

Slots

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 of the Class

An instance is created by calling function runMLwiN.

Author(s)

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

See Also

runMLwiN

Examples

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## 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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