| Title: | Produces an Odds Ratio Plot from a Logistic Regression Model |
|---|---|
| Description: | Produces an Odds Ratio (OR) Plot to visualise the result of a logistic regression analysis. Provide it with a binomial regression model produced by 'glm()' and it will convert the estimates to odds ratios with a 95% confidence interval and plot the results using 'ggplot2'. |
| Authors: | Craig Parylo [aut, cre, cph] |
| Maintainer: | Craig Parylo <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.5.2 |
| Built: | 2025-02-09 11:16:40 UTC |
| Source: | https://github.com/craig-parylo/plotor |
Produces an Odds Ratio plot to visualise the results of a logistic regression analysis.
plot_or(glm_model_results, conf_level = 0.95)plot_or(glm_model_results, conf_level = 0.95)
glm_model_results |
Results from a binomial Generalised Linear Model (GLM), as produced by |
conf_level |
Numeric between 0.001 and 0.999 (default = 0.95). The confidence level to use when setting the confidence interval, most commonly will be 0.95 or 0.99 but can be set otherwise. |
plotor returns an object of class gg and ggplot
See vignette('using_plotor', package = 'plotor') for more details on use.
More details and examples are found on the website: https://craig-parylo.github.io/plotor/index.html
# libraries library(plotor) library(datasets) library(dplyr) library(ggplot2) library(stats) library(forcats) library(tidyr) # get some data df <- datasets::Titanic |> as_tibble() |> # convert aggregated counts to individual observations filter(n > 0) |> uncount(weights = n) |> # convert character variables to factors mutate(across(where(is.character), as.factor)) # perform logistic regression using `glm` lr <- glm( data = df, family = 'binomial', formula = Survived ~ Class + Sex + Age ) # produce the Odds Ratio plot plot_or(lr)# libraries library(plotor) library(datasets) library(dplyr) library(ggplot2) library(stats) library(forcats) library(tidyr) # get some data df <- datasets::Titanic |> as_tibble() |> # convert aggregated counts to individual observations filter(n > 0) |> uncount(weights = n) |> # convert character variables to factors mutate(across(where(is.character), as.factor)) # perform logistic regression using `glm` lr <- glm( data = df, family = 'binomial', formula = Survived ~ Class + Sex + Age ) # produce the Odds Ratio plot plot_or(lr)
{glm} modelCheck whether the glm model object is the product of logistic regression.
validate_glm_model(glm_model)validate_glm_model(glm_model)
glm_model |
Results from a binomial Generalised Linear Model (GLM), as produced by |
boolean (TRUE = logistic regression, FALSE = other model)