summary.glmulti: Handling glmulti objects
In glmulti: Model Selection and Multimodel Inference Made Easy

Description Usage Arguments Details Value Author(s) References See Also

These standard S3 functions can be applied on glmulti to print a short report, obtain a more detailed summary, or produce different types of graphics.

# S3 summary method for class 'glmulti'
## S3 method for class 'glmulti'
summary(object, ...)

# S3 print method for class 'glmulti'
## S3 method for class 'glmulti'
print(x, ...)


# S3 plot method for class 'glmulti'
## S3 method for class 'glmulti'
plot(x, type="p", ...)

`object`	A `glmulti` object
`x`	An object of class `glmulti`
`type`	The type of graph to be produced. One of "p", "r", "s" or "w" (see below).
`...`	Further arguments.

The name of the returned object is the name of the first object passed, with "consensus-" prepended. The params slot of the returned object is taken from the first object too.

plot can be used to have a graphical representation of the results. Two types are proposed:

type="p" plots the IC profile (the IC values form the best to the worst model). A horizontal line delineates models that are less than 2 IC units away from the best model.

type="r" shows diagnostics of the fit (residuals versus predicted values, and QQ plot of residuals) for the five best models. It calls the plot functions on the fitted model objects, which should be defined (e.g. plot.lm, plot.glm). This type of plot can only be used if model objects are included in the glmulti object (i.e. if includeobjects was set to true).

type="s" plots the relative importance of model terms, i.e. the overall support for each variable across all models. A vertical line is drawn at 80

type="w" plots the normalized evidence weights of the models. A vertical line delineates models that sum um to 95

print prints a brief synthesis of the analysis (e.g. the best model found, its IC value and evidence weight...)

summary returns a list with more detailed elements:

`name`	the name of the analysis
`method`	The method used
`fitting`	The fitting function used
`crit`	The IC used
`level`	Whether interactions between predictors were considered or not
`marginality`	Whether the marginality rule was applied
`confsetsize`	The requested size of the confidence set
`bestic`	The lowest IC found
`icvalues`	The IC values of the models in the confidence set, from lower to greater
`bestmodel`	A list containing the formula of the best model found, or of the best models found if several had the same IC value
`generations`	The number of generations it took to converge. For genetic algorithm only.
`elapsed`	The actual (system) time it took. For genetic algorithm only.
`includeobjects`	A boolean indicating whether fitted model objects are contained in the object.