| 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: | 0.5.2.9000 |
| Built: | 2025-03-09 12:27:45 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. |
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)
Produces a formatted table showing the outputs from the Odds Ratio analysis.
table_or(glm_model_results, conf_level = 0.95, output = "tibble")table_or(glm_model_results, conf_level = 0.95, output = "tibble")
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. |
output |
String description of the output type. Default = 'tibble'. Options include 'tibble' and 'gt'. |
Includes details on the characteristics of the covariates, such as:
the number of observations for each characteristic,
the number of observations resulting in the outcome of interest,
the conversion rate of outcome by the number of observations,
Details are calculated showing the:
estimated Odds Ratio, standard error and p-value,
calculated confidence interval for the confidence level,
Also included is a visualisation of the OR plot to provide an at-a-glance view of the model results.
object returned depends on output parameter - output = 'tibble' returns an object of "tbl_df", "tbl", "data.frame" class, whilst output = 'gt' returns an object of class "gt_tbl" and "list".
# get some data df <- datasets::Titanic |> dplyr::as_tibble() |> # convert aggregated counts to individual observations dplyr::filter(n > 0) |> tidyr::uncount(weights = n) |> # convert character variables to factors dplyr::mutate(dplyr::across(dplyr::where(is.character), as.factor)) # perform logistic regression using `glm` lr <- stats::glm( data = df, family = 'binomial', formula = Survived ~ Class + Sex + Age ) # produce the Odds Ratio table, first as a tibble then as gt object table_or(lr) table_or(lr, output = 'gt')# get some data df <- datasets::Titanic |> dplyr::as_tibble() |> # convert aggregated counts to individual observations dplyr::filter(n > 0) |> tidyr::uncount(weights = n) |> # convert character variables to factors dplyr::mutate(dplyr::across(dplyr::where(is.character), as.factor)) # perform logistic regression using `glm` lr <- stats::glm( data = df, family = 'binomial', formula = Survived ~ Class + Sex + Age ) # produce the Odds Ratio table, first as a tibble then as gt object table_or(lr) table_or(lr, output = 'gt')