View source: R/many_regression_models.R

Many approximate simple logistic regressions | R Documentation |

Many approximate simple logistic regressions.

```
sp.logiregs(y, x, logged = FALSE)
```

`y` |
The dependent variable, a numerical vector with 0s or 1s. |

`x` |
A matrix with the indendent variables. |

`logged` |
Should the p-values be returned (FALSE) or their logarithm (TRUE)? |

Many simple approximate logistic regressions are performed and hypothesis testing for the singificance of each coefficient is returned. The code is available in the paper by Sikorska et al. (2013). We simply took the code and made some minor modifications. The explanation and the motivation can be found in their paper. They call it semi-parallel logistic regressions, hence we named the function sp.logiregs.

A two-column matrix with the test statistics (Wald statistic) and their associated p-values (or their logarithm).

Initial author Karolina Sikorska. Modifications by Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

Karolina Sikorska, Emmanuel Lesaffre, Patrick FJ Groenen and Paul HC Eilers (2013): 14:166. GWAS on your notebook: fast semi-parallel linear and logistic regression for genome-wide association studies. https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-14-166

` logiquant.regs, bic.regs `

```
y <- rbinom(200, 1, 0.5)
x <- matrix( rnorm(200 * 50), ncol = 50 )
a <- sp.logiregs(y, x)
```

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