R2MLwiN (version 0.8-1)

predLines: Draws predicted lines using a fitted model object

Description

This function draws predicted lines against an explanatory variable for selected groups at a higher (>=2) level.

Usage

predLines(object, indata = NULL, xname, lev = 2, selected = NULL,
  probs = c(0.025, 0.975), legend = TRUE, legend.space = "top",
  legend.ncol = 4, ...)

Arguments

object

Either an mlwinfitIGLS-class or mlwinfitMCMC-class object.

indata

A data.frame object containing the data. If not specified, data is extracted from the object.

xname

The name of the variable to be plotted.

lev

A digit indicating the level (of the multilevel model) at which to plot.

selected

A vector specifying groups to selectively plot at the level specified in lev. If selected = NULL, then all groups at that level are included.

probs

A numeric vector of probabilities with values in [0, 1] used to calculate the lower and upper quantiles from which the error bars are plotted. Currently, this is only available for an mlwinfi

legend

A logical value indicating whether a legend is to be added.

legend.space

A character string specifies one of the four sides, which can be one of 'top', 'bottom', 'left' and 'right'. Default, legend.space = 'top'.

legend.ncol

An integer specifies a number of columns, possibly divided into blocks, each containing some rows. Default, legend.ncol = 2.

...

Other arguments to be pased to xyplot.

See Also

predCurves

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/')

## Example: tutorial
data(tutorial, package = "R2MLwiN")
(mymodel <- runMLwiN(normexam ~ 1 + standlrt + (1 + standlrt | school) + (1 | student),
                     estoptions = list(EstM = 1, resi.store.levs = 2), data = tutorial))

predLines(mymodel, xname = "standlrt", lev = 2, selected = c(30, 44, 53, 59),
          probs = c(0.025, 0.975))