plot.lologGmm: Conduct Monte Carlo diagnostics on a lolog model fit

In lolog: Latent Order Logistic Graph Models

Description

This function creates simple diagnostic plots for MC sampled statistics produced from a lolog fit.

Usage

## S3 method for class 'lologGmm'
plot(x, type = c("histograms", "target", "model"), ...)

Arguments

x	A model fit object to be diagnosed.
type	The type of diagnostic plot. "histograms", the default, produces histograms of the sampled output statistic values with the observed statistics represented by vertical lines. "target" produces a pairs plot of the target output statistic values with the pairs of observed target statistics represented by red squares. output statistic values with the observed statistics represented by vertical lines. "model" produces a pairs plot of the sampled output statistic values with the pairs of observed statistics represented by red squares.
...	Additional parameters. Passed to geom_histogram if type="histogram" and pairs otherwise.

Details

Plots are produced that represent the distributions of the output sampled statistic values or the target statistics values. The values of the observed target statistics for the networks are also represented for comparison with the sampled statistics.

Examples

library(network)
set.seed(1)
data(flo)
flomarriage <- network(flo,directed=FALSE)
flomarriage %v% "wealth" <- c(10,36,27,146,55,44,20,8,42,103,48,49,10,48,32,3)


# An order dependent model
fit3 <- lolog(flomarriage ~ edges + nodeCov("wealth") + preferentialAttachment(),
              flomarriage ~ star(2:3), verbose=FALSE)
plot(fit3)
plot(fit3, "target")
plot(fit3, "model")

lolog documentation built on July 1, 2021, 9:09 a.m.