Description Slots An instance of the Class Author(s) See Also Examples

An MLwiN model run via the MCMC estimation method is represented by an "mlwinfitMCMC" 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.

`burnin`

An integer specifying length of the burn-in.

`nchains`

An integer specifying number of MCMC chains run.

`iterations`

An integer specifying the number of iterations after burn-in.

`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.

`merr`

A vector which sets-up measurement errors on predictor variables.

`fact`

A list of objects specified for factor analysis, including

`nfact`

,`lev.fact`

,`nfactor`

,`factor`

,`loading`

and`constr`

.`xc`

A list of objects specified for cross-classified and/or multiple membership models, including

`class`

,`N1`

,`weight`

,`id`

and`car`

.`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.

`chains`

Captures the MCMC chains from MLwiN for all parameters.

`elapsed.time`

Calculates the CPU time used for fitting the model.

`BDIC`

Bayesian Deviance Information Criterion (DIC)

`call`

The matched call.

`LIKE`

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

`fact.loadings`

If

`fact`

is not empty, then the factor loadings are returned.`fact.loadings.sd`

If

`fact`

is not empty, then the factor loading standard deviationss are returned.`fact.cov`

If

`fact`

is not empty, then factor covariances are returned.`fact.cov.sd`

If

`fact`

is not empty, then factor covariance standard deviations are returned.`fact.chains`

If

`fact`

is not empty, then the factor chains are returned.`MIdata`

If

`dami[1]`

is one then the mean complete response variable`y`

is returned for each chain, if`dami[1]`

is two then the SD is also included.`imputations`

If

`dami[1]`

is zero, then a list of completed datasets containing complete response variable`y`

is returned.`residual`

If

`resi.store`

is`TRUE`

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

If

`resi.store.levs`

is not empty, then the residual chains at these levels are returned.`version`

The MLwiN version used to fit the model

`data`

The data.frame that was 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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
## 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),
estoptions = list(EstM = 1), data = tutorial))
##summary method
summary(mymodel)
##BDIC slot
mymodel@BDIC
## End(Not run)
``` |

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