plot.cv_sgdnet: Plot Results from Cross-Validation

In jolars/sgdnet: Fast Sparse Linear Models for Big Data with SAGA

Description

Plot Results from Cross-Validation

Usage

## S3 method for class 'cv_sgdnet'
plot(x, sign.lambda = 1, plot_min = TRUE,
  plot_1se = TRUE, ci_alpha = 0.2, ci_border = FALSE,
  ci_col = lattice::trellis.par.get("superpose.line")$col, ...)

Arguments

x	an object of class 'cv_sgdnet'
sign.lambda	the sign of lambda
plot_min	whether to mark the location of the lambda corresponding to the best prediction score
plot_1se	whether to mark the location of the largest lambda within one standard deviation from the location corresponding to plot_min
ci_alpha	alpha (opacity) for fill in confidence limits
ci_border	color (or flag to turn off and on) the border of the confidence limits
ci_col	color for border of confidence limits
...	other arguments that are passed on to lattice::xyplot()

Value

An object of class 'trellis' is returned and, if used interactively, will most likely have its print function lattice::print.trellis()) invoked, which draws the plot on the current display device.

See Also

cv_sgdnet, lattice::xyplot(), lattice::panel.xyplot()

Examples

cv <- cv_sgdnet(heart$x,
                heart$y,
                family = "binomial",
                alpha = c(0, 0.8, 0.9))
plot(cv, ci_alpha = 0.3, plot_1se = FALSE)

jolars/sgdnet documentation built on May 22, 2019, 11:52 p.m.