# T-Test for regression coefficients

### Description

This function computes test statistics and p-values for the regression coefficients of Penalized Partial Least Squares.

### Usage

1 | ```
ttest.ppls(ppls.object,ncomp,index.lambda)
``` |

### Arguments

`ppls.object` |
an object returned by |

`ncomp` |
integer. The number of components that are used. The default value is the cross-validation optimal number of components. |

`index.lambda` |
integer. The index of the penalization intensity, given by the vector |

### Details

We note that neither the distribution of the regression coefficients nor the correct degrees of freedom are known. Hence, the assumptions of the T-Test might not be fulfilled. We remark that this testing procedure is not discussed in Kraemer, Boulesteix and Tutz (2008). In general, the p-values need to be corrected in order to account for the multiple testing problem.

### Value

`tvalues` |
vector of test statistics |

`pvalues` |
vector of p-values |

### Author(s)

Nicole Kraemer

### References

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

### See Also

`penalized.pls.cv`

,`jack.ppls`

### Examples

1 2 3 4 5 6 7 8 | ```
data(cookie) # load data
X<-as.matrix(cookie[,1:700]) # extract NIR spectra
y<-as.vector(cookie[,701]) # extract one constituent
pls.object<-penalized.pls.cv(X,y,ncomp=10,kernel=TRUE) # PLS without penalization
my.ttest<-ttest.ppls(pls.object) # test for the cv-optimal model
plot(sort(my.ttest$pvalues),type="l",ylab="sorted pvalues") # plot sorted p-values
``` |

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