plot {crisp}

Plots Fit from crisp or crispCV.

Description

This function plots fit of the class crispCV, or class crisp with a user-specified tuning parameter.

Usage

## S3 method for class 'crisp'
plot(x, lambda.index, title = NULL, x1lab = NULL,
  x2lab = NULL, min = NULL, max = NULL, cex.axis = 1, cex.lab = 1,
  color1 = "seagreen1", color2 = "steelblue1", color3 = "darkorchid4",
  ...)

## S3 method for class 'crispCV'
plot(x, title = NULL, x1lab = NULL, x2lab = NULL,
  min = NULL, max = NULL, cex.axis = 1, cex.lab = 1,
  color1 = "seagreen1", color2 = "steelblue1", color3 = "darkorchid4",
  ...)

Arguments

x

An object of class crisp or crispCV, which result from running the crisp or crispCV functions, respectively.

lambda.index

The index for the desired value of lambda, i.e., x$lambda.seq[lambda.index].

title

The title of the plot. By default, the value of lambda is noted.

x1lab

The axis label for the first covariate. By default, it is "X1".

x2lab

The axis label for the second covariate. By default, it is "X2".

min, max

The minimum and maximum y-values, respectively, to use when plotting the fit. By default, they are chosen to be the minimum and maximum of all of the fits, i.e., the minimum and maximum of unlist(x$M.hat.list).

cex.axis

The magnification to be used for axis annotation relative to the current setting of cex.

cex.lab

The magnification to be used for x and y labels relative to the current setting of cex.

color1, color2, color3

The colors to use to create the color gradient for plotting the response values. At least the first two must be specified, or the defaults of "seagreen1", "steelblue1", and "darkorchid4" will be used.

...

Additional arguments to be passed, which are ignored in this function.

Value

None.

Examples

## Not run: 
#See ?'crisp-package' for a full example of how to use this package

#generate data (using a very small 'n' for illustration purposes)
set.seed(1)
data <- sim.data(n = 15, scenario = 2)

#fit model for a range of tuning parameters, i.e., lambda values
#lambda sequence is chosen automatically if not specified
crisp.out <- crisp(X = data$X, y = data$y)
#or fit model and select lambda using 2-fold cross-validation
#note: use larger 'n.fold' (e.g., 10) in practice
crispCV.out <- crispCV(X = data$X, y = data$y, n.fold = 2)

#plot the estimated relationships between two predictors and outcome
#do this for a specific fit
plot(crisp.out, lambda.index = 25)
#or for the fit chosen using cross-validation
plot(crispCV.out)

## End(Not run)