## Not run:
# #general example illustrating all functions
# #see specific function help pages for details of using each function
#
# #generate data (using a very small 'n' for illustration purposes)
# set.seed(1)
# data <- sim.data(n = 15, scenario = 2)
# #plot the mean model for the scenario from which we generated data
# plot(data)
#
# #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)
#
# #summarize all of the fits
# summary(crisp.out)
# #or just summarize a single fit
# #we examine the fit with an index of 25. that is, lambda of
# crisp.out$lambda.seq[25]
# summary(crisp.out, lambda.index = 25)
# #lastly, we can summarize the fit chosen using cross-validation
# summary(crispCV.out)
# #and also plot the cross-validation error
# plot(summary(crispCV.out))
# #the lambda chosen by cross-validation is also available using
# crispCV.out$lambda.cv
#
# #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)
#
# #we can make predictions for a covariate matrix with new observations
# #new.X with 20 observations
# new.data <- sim.data(n = 20, scenario = 2)
# new.X <- new.data$X
# #these will give the same predictions:
# yhat1 <- predict(crisp.out, new.X = new.X, lambda.index = crispCV.out$index.cv)
# yhat2 <- predict(crispCV.out, new.X = new.X)
# ## End(Not run)
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