plot_spline function

Plot spline curve for a fitted additive model

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

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

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.

• add: Add spline to existing plot? (default: FALSE)

• ...: 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)