Formula.translate: An internal function to translate an R formula into an R list...

In R2MLwiN: Running 'MLwiN' from Within R

An internal function to translate an R formula into an R list object.

Description

A model formula, as a formula object written in R-type syntax, is translated into an R list object.

Usage

Formula.translate(Formula, D = "Normal", indata)

Arguments

Formula	A formula object specifying a multilevel model. See formula for notes on general usage, and runMLwiN for further details.
D	A character string/vector specifying the type of distribution to be modelled, which can include 'Normal' (the default), 'Binomial', 'Poisson', 'Negbinom', 'Unordered Multinomial', 'Ordered Multinomial', 'Multivariate Normal', or 'Mixed'. In the case of the latter, 'Mixed' precedes the response types which also need to be listed in D, e.g. c('Mixed', 'Normal', 'Binomial'); these need to be be listed in the same order to which they are referred to in the Formula object.
indata	A data.frame object containing the data to be modelled. Optional (can attach as an alternative) but recommended.

Value

Outputs an R list object, which is then used as the input for write.IGLS or write.MCMC.

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, write.IGLS, write.MCMC; for function allowing back-compatibility with Formula syntax used in older versions of R2MLwiN (<0.8.0) see Formula.translate.compat.

Examples

## Not run: 
# NB: See demo(packge = 'R2MLwiN') for a wider range of examples.
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/')

# Two-level random intercept model with student (level 1) nested within
# school (level 2) and standlrt added to the fixed part.
# Importantly, the ordering of school and student reflects their hierarchy,
# with the highest level (school) specified first.
# E.g. see demo(UserGuide04)
data(tutorial, package = 'R2MLwiN')
(mymodel1 <- runMLwiN(normexam ~ 1 + standlrt + (1 | school) + (1 | student),
                     data = tutorial))

# Adding a random slope
(mymodel2 <- runMLwiN(normexam ~ 1 + standlrt + (1 + standlrt | school)
                     + (1 | student), data = tutorial))

# Exploring complex level 1 variation
# E.g. see demo(UserGuide07)
(mymodel3 <- runMLwiN(normexam ~ 1 + standlrt + (1 + standlrt | school)
                      + (1 + standlrt | student), data = tutorial))

# Logit link with cons specified as denominator
# Note level 1 ID not explicitly specified
# E.g. see demo(UserGuide09)
data(bang, package = 'R2MLwiN')
(mymodel4 <- runMLwiN(logit(use, cons) ~ 1 + lc + age + (1 | district),
                      D = 'Binomial', data = bang))

# Mixed response model
# Note using MCMC estimation (EstM = 1)
# Normal (english) and Bernoulli (behaviour) distributed responses
# probit link modelling behaviour with cons as denominator
# E.g. see demo(MCMCGuide19)
data(jspmix1, package = 'R2MLwiN')
(mymodel <- runMLwiN(c(english, probit(behaviour, cons)) ~
                     1 + sex + ravens + fluent[1] + (1 | school) + (1[1] | id),
                     D = c('Mixed', 'Normal', 'Binomial'),
                     estoptions = list(EstM = 1,
                     mcmcMeth = list(fixM = 1, residM = 1, Lev1VarM = 1)),
                     data = jspmix1))

## End(Not run)

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