View source: R/many_regression_models_correlations.R

Many univariate simple logistic and Poisson regressions | R Documentation |

It performs very many univariate simple binary logistic regressions.

logistic_only(x, y, tol = 1e-09, b_values = FALSE) poisson_only(x, y, tol = 1e-09, b_values = FALSE)

`x` |
A matrix with the data, where the rows denote the samples (and the two groups) and the columns are the variables. Currently only continuous variables are allowed. |

`y` |
The dependent variable; a numerical vector with two values (0 and 1) for the logistic regressions and a vector with many discrete values (count data) for the Poisson regressions. |

`tol` |
The tolerance value to terminate the Newton-Raphson algorithm. |

`b_values` |
Do you want the values of the coefficients returned? If yes, set this to TRUE. |

The function is written in C++ and this is why it is very fast. It can accept thousands of predictor variables. It is usefull for univariate screening. We provide no p-value correction (such as fdr or q-values); this is up to the user.

A vector with the deviance of each simple binayr logistic regression model for each predictor variable.

Manos Papadakis <papadakm95@gmail.com>

R implementation and documentation: Michail Tsagris <mtsagris@uoc.gr> and Manos Papadakis <papadakm95@gmail.com>.

McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.

```
univglms, score.glms, prop.regs, quasi.poisson_only, allbetas, correls, regression
```

## 300 variables, hence 300 univariate regressions are to be fitted x <- matrix( rnorm(100 * 300), ncol = 300 ) ## 100 observations in total y <- rbinom(100, 1, 0.6) ## binary logistic regression a1 <- logistic_only(x, y) a2 <- glm(y ~ x[, 1], binomial)$deviance a2 <- as.vector(a2) y <- rpois(100, 10) a1 <- poisson_only(x, y) a1 <- x <- NULL

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