Model a binary dependent response variable as a relationship between one or more explanatory variables by fitting a logistic regression model.

## Details

A binomial logistic regression models a binary dependent variable with two outcomes by one or more independent or predictor variables. If the binary outcomes in the dependent variable are labelled ‘0’ and ‘1’ then fitted and predicted values of a logistic model are the log-odds for the value labelled ‘1’. The log-odds can be converted to a probability by the logistic function with a probability 1 being “certainly belonging to ‘1’”.

In this module, if a dependent variable with more than two values is selected, then the values in the dependent variable will be revalued so that the first level represents a failure and the rest represent a success.

## Output

The module output is the `summary`

output of a linear model created in R. More details on this output can be found in the R documentation for `glm`

.

## Parameters

Variable name | Required | Constraints | Description |
---|---|---|---|

outcome_var | Yes | Column Input.
Text, Boolean, Integer |
A binary variable to be used as the dependent variable in the model. |

model_var1 | Yes | Any column other than the column chosen for outcome_var. | A required first independent variable to include in the model. |

model_var2 | No | Any column other than the column chosen for outcome_var. | An optional second predictor variable. |

model_var3 | No | Any column other than the column chosen for outcome_var. | An optional third predictor variable. |

model_var4 | No | Any column other than the column chosen for outcome_var. | An optional fourth predictor variable. |

model_var5 | No | Any column other than the column chosen for outcome_var. | An optional fifth predictor variable. |

include_intercept | Yes | Boolean | Whether to include an intercept term in the model |