plot.BranchGLMVS {BranchGLM}

R Documentation

Plot Method for summary.BranchGLMVS and BranchGLMVS objects

Description

Plot Method for summary.BranchGLMVS and BranchGLMVS objects

Usage

## S3 method for class 'BranchGLMVS'
plot(
  x,
  ptype = "both",
  marnames = 7,
  addLines = TRUE,
  type = "b",
  horiz = FALSE,
  cex.names = 1,
  cex.lab = 1,
  cex.axis = 1,
  cex.legend = 1,
  ...
)

## S3 method for class 'summary.BranchGLMVS'
plot(
  x,
  ptype = "both",
  marnames = 7,
  addLines = TRUE,
  type = "b",
  horiz = FALSE,
  cex.names = 1,
  cex.lab = 1,
  cex.axis = 1,
  cex.legend = 1,
  ...
)

Arguments

x	a summary.BranchGLMVS or BranchGLMVS object.
ptype	the type of plot to produce, look at details for more explanation.
marnames	value used to determine how large to make margin of axis with variable names, this is only for the "variables" plot. If variable names are cut-off, consider increasing this from the default value of 7.
addLines	logical value to indicate whether or not to add black lines to separate the models for the "variables" plot. This is typically useful for smaller amounts of models, but can be annoying if there are many models.
type	what type of plot to draw for the "metrics" plot, see more details at plot.default.
horiz	whether models should be displayed horizontally or vertically in the "variables" plot.
cex.names	how big to make variable names in the "variables" plot.
cex.lab	how big to make axis labels.
cex.axis	how big to make axis annotation.
cex.legend	how big to make legend labels.
...	arguments passed to the generic plot and image methods.

Details

The different values for ptype are as follows

• "metrics" for a plot that displays the metric values ordered by rank

• "variables" for a plot that displays which variables are in each of the top models

• "both" for both plots

Value

This only produces plots, nothing is returned.

Examples

Data <- iris
Fit <- BranchGLM(Sepal.Length ~ ., data = Data, family = "gaussian", link = "identity")

# Doing branch and bound selection 
VS <- VariableSelection(Fit, type = "branch and bound", metric = "BIC", bestmodels = 10, 
showprogress = FALSE)
VS

## Getting summary of the process
Summ <- summary(VS)
Summ

## Plotting the BIC of the best models
plot(Summ, type = "b")

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