Multi-trait SGP accuracy vs penalization plot {SFSI}

Accuracy vs penalization from multi-trait SGP

Description

Visualizing results from an object of the class 'SGP'

Usage

multitrait.plot(object, trait_names = NULL,
                x.stat = c("nsup","lambda"),
                y.stat = c("accuracy","MSE"), label = x.stat,
                line.color = "orange", point.color = line.color,
                point.size = 1.2, nbreaks.x = 6, ...)

Arguments

object

An object of the class 'SGP' for a multi-trait case

x.stat

(character) Either 'nsup' (number of non-zero regression coefficients entering in the prediction of a given testing individual) or 'lambda' (penalization parameter in log scale) to plot in the x-axis

y.stat

(character) Either 'accuracy' (correlation between observed and predicted values) or 'MSE' (mean squared error) to plot in the y-axis

label

(character) Similar to x.stat but to show the value in x-axis for which the y-axis is maximum across traits

point.color, line.color

(character) Color of the points and lines

point.size

(numeric) Size of the points showing the maximum accuracy

nbreaks.x

(integer) Number of breaks in the x-axis

trait_names

(character) Names of traits to be shown in the plot

...

Other arguments for method plot: 'xlab', 'ylab', 'main', 'lwd', 'xlim', 'ylim'

Value

Creates a plot of either accuracy or MSE versus either the support set size (average number of predictors with non-zero regression coefficient) or versus lambda. This is done separately for each trait

Examples

  # See examples in
  # help(SGP, package="SFSI")