mgm (version 1.2-2)

plotRes: Plot summary of resampled sampling distributions

Description

Plots a summary of sampling distributions resampled with the resample() function

Usage

plotRes(object, qtl, labels = NULL, decreasing = TRUE, 
        cut = NULL, cex.label = 0.75, lwd.qtl = 2, 
        cex.mean = 0.5, cex.bg = 3.5, 
        axis.ticks = c(-0.5, -0.25, 0, 0.25, 0.5, 0.75, 1))

Arguments

object

An output object from the resample() function.

qtl

A numerical vector of length two specifying the desired lower/upper quantile, e.g. c(.05, .95).

labels

A character vector of length p containing the label of each variable, where p is the number of variables.

decreasing

If TRUE (default), the edges are displayed by the mean of the sampling distribution in decreasing order. If FALSE they are ordered in increasing order.

cut

A sequence of integers, specifying which edges are represented. For instance, if decreasing = TRUE and cut = 1:10, summaries for the 10 edges with the largest parameter estimate are displayed.

cex.label

Text size of the labels.

lwd.qtl

Line width of line indicating the upper/lower quantiles.

cex.mean

Text size of the number indicating the proportion of the estimates whose absolute value is larger than zero.

cex.bg

Size of the white background of the number indicating the proportion of the estimates whose absolute value is larger than zero.

axis.ticks

A numeric vector indicating the axis ticks and labels for the x-axis.

Value

Plots a figure that shows summaries of the resampled sampling distribution for (a set of) all edge parameters. These include the mean, a specified upper and lower quantile and the proportion of parameter estimates whose absolute value is larger than zero.

Details

Currently only supports summaries for resampled mgm() objects.

See Also

resample(), mgm(), mvar(), tvmgm(), tvmar()

Examples

# NOT RUN {
# }
# NOT RUN {
# Fit initial model
fit_aut <- mgm(data = as.matrix(autism_data$data),
              type = autism_data$type,
              level = autism_data$lev,
              k = 2)


# Fit bootstrapped models
res_aut <- resample(object = fit_aut, 
                    data = as.matrix(autism_data$data), 
                    nB = 10)

# Plot Summary
plotRes(object = res_aut,
        qtl = c(.05, .95), 
        labels = NULL, 
        axis.ticks = c(-.25, 0, .25, .5, .75))

  
# }