Description Usage Arguments Value Examples

This has been created to generate odds plots on the back of results from a generalised linear model.

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`x` |
The trained caret GLM logistic regression model |

`x_label` |
The label name for the x_label |

`y_label` |
The label name for the y_label |

`title` |
Title for the Odds Plot |

`subtitle` |
Subtitle for the Odds Plot |

`point_col` |
Defaults to blues, but R colour codes can be passed |

`error_bar_colour` |
the colour of the error bar |

`point_size` |
the point size of the plot |

`error_bar_width` |
the width of the displayed error bar |

`h_line_color` |
the colour of the horizontal line |

A list of the odds returned from logistic regression and a plot showing the odds

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
#We will use the cancer dataset to build a GLM model to predict cancer status
#this will detail whether the patient has a benign or malignant
library(mlbench)
library(caret)
library(tibble)
library(ggplot2)
library(OddsPlotty)
library(e1071)
library(ggthemes)
#Bring in the data
data("BreastCancer", package = "mlbench")
breast <- BreastCancer[complete.cases(BreastCancer), ]
breast <- breast[, -1]
head(breast, 10)
breast$Class <- factor(breast$Class)
for(i in 1:9) {
breast[, i] <- as.numeric(as.character(breast[, i]))
}
#Train GLM model
glm_model <- train(Class ~ ., data = breast, method = "glm", family = "binomial")
#Visualise the data with OddsPlotty
plotty <- OddsPlotty::odds_plot(glm_model$finalModel,title = "Odds Plot")
plotty$odds_plot
#Extract underlying odds ratios
plotty$odds_data
``` |

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