opt_plot: Makes a line plot of the results from the optimization tests
In jillbo1000/EZtuneTest:

Description Usage Arguments Value See Also

opt_plot creates a line plot with the results of the optimization tests.

1	opt_plot(dat, value, facet = "optimizer", cols = NULL, title = NULL)

`dat`	Dataset created from load_opt_data or with several datasets from load_opt_data combined with rbind.
`value`	Specifies which error metric should be plotted. This should be the name of a variable in the dataset, dat.
`facet`	Specifies a faceting variable. The typical faceting variable is optimizer, but other options include data or method.
`cols`	A color palette to use with the data. If it is not specified, the Dark2 palette from color brewer will be used.
`title`	Optional character string to use a title for the plot.

Returns a parallel coordinate plot created by ggplot. The x-axis shows the different optimization methods used, the y-axis shows the standardized error rate for each dataset. Within each dataset, the optimization method with the largest loss is assigned a value of 1 and the smallest is assigned a value of 0. If a faceting variable is selected, standardization will be done regardless of the faceted variable. Standardizing is done within each dataset so each dataset should have a value of 1 and a value of 0 on the graph. Datasets that are all NA for an optimization method are denoted with a star and a value of 1.

load_opt_data, average_metric

jillbo1000/EZtuneTest documentation built on Oct. 5, 2021, 4:16 p.m.