plotting device for penalized PLS on splines transformed variables

1 2 3 4 5 | ```
graphic.ppls.splines(X,y,lambda,
add.data,select,ncomp,deg,
order,nknot,reduce.knots,
kernel,window.size)
``` |

`X` |
matrix of input data |

`y` |
vector of response data |

`add.data` |
logical value. If TRUE, the data |

`select` |
Logical value. If |

`lambda` |
vector of candidate parameters lambda for the penalty term. Default value is NULL |

`ncomp` |
Number of PLS components, default value is 1 |

`deg` |
Degree of the splines. Default value is 3 |

`order` |
Order of the differences to be computed for the penalty term. Default value is 2. |

`nknot` |
number of knots. Default value is 20 for all variables. |

`kernel` |
Logical value. If kernel=TRUE, the kernelized version of penalized PLS is computed. Default value is kernel=TRUE |

`reduce.knots` |
Logical variable. If |

`window.size` |
vector of length size 2. Determines the number of plots on one page. Default is c(3,3), that is 3 rows and 3 columns. |

This function computes a nonlinear regression
model with Penalized Partial Least Squares using penalized PLS on B-spline transformed variables. The model parameters have to be provided - for proper model selection, we recommend to determine the optiaml parameters with `ppls.splines.cv`

. Consult Kraemer, Boulesteix, and Tutz (2008) for details.

The function plots the additive components for each variable.

WARNING: If add.data=TRUE, the function also plots the data X and y. While it seems convenient to compare the data (x_j,y) and the fitted functions (x_j,f_j(x_j)), one should keep in mind that only the sum of the fitted functions f_j(x) are an approximation of y.

`ppls.coefficients` |
The regression coefficients for the transformed variables. |

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`

,`X2s`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
# ------------------------------------------------------
# load boston housing data
library(MASS)
data(Boston)
y<-Boston[,14]
X<-Boston[,-14]
X<-X[,-4] # remove categorical variable
X<-as.matrix(X)
# ----------------------------------------------------------------------
# plot ppls results for some random parameters
# with variable selection , and with data (add.data=TRUE)
dummy<-graphic.ppls.splines(X,y,lambda=100,ncomp=5,add.data=TRUE,select=TRUE,window.size=c(3,4))
# without variable selection and without data
dummy<-graphic.ppls.splines(X,y,lambda=100,ncomp=5,add.data=FALSE,select=FALSE,window.size=c(3,4))
``` |

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