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

Function that applies a bootstrap based test for covariate selection. It helps to determine the number of variables to be included in the model.

1 2 3 |

`x` |
A data frame containing all the covariates. |

`y` |
A vector with the response values. |

`method` |
A character string specifying which regression method is used,
i.e., linear models ( |

`family` |
A description of the error distribution and link function to be
used in the model: ( |

`nboot` |
Number of bootstrap repeats. |

`speedup` |
A logical value. If |

`qmin` |
By default |

`unique` |
A logical value. By default |

`q` |
By default |

`bootseed` |
Seed to be used in the bootstrap procedure. |

`cluster` |
A logical value. If |

`ncores` |
An integer value specifying the number of cores to be used
in the parallelized procedure. If |

In a regression framework, let *X_1, X_2, …, X_p*, a set of
*p* initial variables and *Y* the response variable, we propose a
procedure to test the null hypothesis of *q* significant variables in
the model –*q* effects not equal to zero– versus the alternative in
which the model contains more than *q* variables. Based on the general
model

*Y=m(\textbf{X})+\varepsilon \quad {\rm{where}} \quad
m(\textbf{X})= m_{1}(X_{1})+m_{2}(X_{2})+…+m_{p}(X_{p})*

the following
strategy is considered: for a subset of size *q*, considerations will be
given to a test for the null hypothesis

*H_{0} (q): ∑_{j=1}^p
I_{\{m_j \ne 0\}} ≤ q*

vs. the general hypothesis

*H_{1} :
∑_{j=1}^p I_{\{m_j \ne 0\}} > q*

A list with two objects. The first one is a table containing

`Hypothesis` |
Number of the null hypothesis tested |

`Statistic` |
Value of the T statistic |

`pvalue` |
pvalue obtained in the testing procedure |

`Decision` |
Result of the test for a significance level of 0.05 |

The second argument `nvar`

indicates the number of variables that
have to be included in the model.

The detailed expression of the formulas are described in HTML help http://cran.r-project.org/web/packages/FWDselect/FWDselect.pdf

Marta Sestelo, Nora M. Villanueva and Javier Roca-Pardinas.

Sestelo, M., Villanueva, N. M. and Roca-Pardinas, J. (2013). FWDselect: an R package for selecting variables in regression models. Discussion Papers in Statistics and Operation Research, University of Vigo, 13/01.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
library(FWDselect)
data(diabetes)
x = diabetes[ ,2:11]
y = diabetes[ ,1]
test(x, y, method = "lm", cluster = FALSE, nboot = 5)
## for speedup = FALSE
# obj2 = qselection(x, y, qvector = c(1:9), method = "lm",
# cluster = FALSE)
# plot(obj2) # we choose q = 7 for the argument qmin
# test(x, y, method = "lm", cluster = FALSE, nboot = 5,
# speedup = FALSE, qmin = 7)
``` |

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