plot_spline {grpreg}

Plot spline curve for a fitted additive model

Description

Plots a spline curve for a single variable using a grpreg or cv.grpreg object for which an additive model was fit.

Usage

plot_spline(
  fit,
  variable,
  lambda,
  which = NULL,
  partial = FALSE,
  type = "contrast",
  warnings = TRUE,
  points.par = NULL,
  ...
)

Arguments

fit

A grpreg object. The model must have been fit using a expand_spline object.

variable

The name of the variable which will be plotted (character).

lambda

Values of the regularization parameter lambda which will be used for the plot. If a vector is passed, a curve will be drawn for each value of lambda (numeric vector; if a cv.grpreg object is passed, the lambda value minimizing cross-validation error will be used as a default; otherwise, there is no default value)

which

Index of penalty parameter lambda which will be used for the plot. If both lambda and which are specified, lambda takes precedence (integer vector).

partial

If TRUE, a scatter plot of the partial residuals is superimposed on the curve (logical; default = FALSE). If multiple lambdas are specified, the largest value is used to calculate the residuals.

type

Type of plot to be produced (default = "contrast"). The following options are supported:

• If "conditional", the plot returned shows the value of the variable on the x-axis and the change in linear predictor on the y-axis, holding all other variables constant at their mean value.

• If "contrast", the plot returned shows the effect on the linear predictor by moving the x variable away from its mean.

warnings

If FALSE, warnings will be suppressed (default = TRUE).

points.par

List of parameters (see par() to pass to points() when partial=TRUE.

...

Further arguments to be passed to plot(). Note that these arguments also control the appearance of the lines.

Details

plot_spline() takes a model fit using both the grpreg() and expand_spline() functions and plots a spline curve for a given variable.

Examples

Data <- gen_nonlinear_data(n=1000)
X <- expand_spline(Data$X)
fit <- grpreg(X, Data$y)
plot_spline(fit, "V02", lambda = 0.03)
plot_spline(fit, "V02", which = c(10, 90))
plot_spline(fit, "V02", lambda = 0.03, partial=TRUE)
plot_spline(fit, "V02", lambda = 0.03, partial=TRUE, type='conditional')
plot_spline(fit, "V02", lambda = 0.03, partial=TRUE, lwd=6, col='yellow',
            points.par=list(pch=9, col='blue'))

op <- par(mfrow=c(3,2), mar=c(4.5, 4.5, 0.25, 0.25))
for (i in 1:6) plot_spline(fit, sprintf("V%02d", i), lambda = 0.03, partial=TRUE)
par(op)

cvfit <- cv.grpreg(X, Data$y)
plot_spline(cvfit, "V02")
plot_spline(cvfit, "V02", partial=TRUE)