sim.data.ppls: Simulated Data
In ppls: Penalized Partial Least Squares

Description Usage Arguments Details Value Author(s) References See Also Examples

generates data that can be used for simulations

1	sim.data.ppls(ntrain,ntest,stnr,p,a=NULL,b=NULL)

`ntrain`	number of training observations
`ntest`	number of test observations
`stnr`	signal to noise ratio
`p`	number of predictor variables
`a`	vector of length 5 that determines the regression problem to be simulated
`b`	vector of length 5 that determines the regression problem to be simulated

The matrix of training and test data is drawn from a uniform distribution over [-1,1] for each of the p variables. The response is generated via a nonlinear regression model of the form

Y=∑ _{j=1} ^5 f_j(X_j) + \varepsilon

where f_j(x)=a_j x + sin(6 b_jx). The values of a_j and b_j can be specified via a or b. If no values for a or b is given, they are drawn randomly from [-1,1]. The variance of the noise term is chosen such that the signal-to-noise-ratio equals stnr on the training data.

`Xtrain`	matrix of size `ntrain` x `p`
`ytrain`	vector of lengt `ntrain`
`Xtest`	matrix of size `ntest` x `p`
`ytest`	vector of lengt `ntest`
`sigma`	standard deviation of the noise term
`a`	vector that determines the nonlinear function
`b`	vector that determines the nonlinear function

Nicole Kraemer

N. Kraemer, A.-L. Boulsteix, and G. Tutz (2008). Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data. Chemometrics and Intelligent Laboratory Systems, 94, 60 - 69. http://dx.doi.org/10.1016/j.chemolab.2008.06.009

ppls.splines.cv