View source: R/many_regression_models_correlations.R

Many simple linear regressions coefficients | R Documentation |

Simple linear regressions coefficients.

```
allbetas(y, x, pvalue = FALSE, logged = FALSE)
```

`y` |
A numerical vector with the response variable. |

`x` |
A matrix with the data, where rows denotes the observations and the columns contain the independent variables. |

`pvalue` |
If you want a hypothesis test that each slope (beta coefficient) is equal to zero set this equal to TRUE. It will also produce all the correlations between y and x. |

`logged` |
A boolean variable; it will return the logarithm of the pvalue if set to TRUE. |

A matrix with the constant (alpha) and the slope (beta) for each simple linear regression. If the p-value is set to TRUE, the correlation of each y with the x is calculated along with the relevant test statistic and its associated p-value.

Michail Tsagris

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

```
mvbetas, correls, univglms, colsums, colVars
```

```
x <- matrix( rnorm(100 * 50), ncol = 50 )
y <- rnorm(100)
r <- cor(y, x) ## correlation of y with each of the xs
a <- allbetas(y, x) ## the coefficients of each simple linear regression of y with x
x <- NULL
```

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