mlwinfitMCMC-class: An S4 class that stores the outputs of the fitted MCMC model.
In R2MLwiN: Running 'MLwiN' from Within R

mlwinfitMCMC-class

R Documentation

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

Description

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

Examples

## 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)

R2MLwiN documentation built on March 31, 2023, 9:17 p.m.