table_or {plotor} | R Documentation |
Table OR
Description
Produces a formatted table showing the outputs from the Odds Ratio analysis.
Usage
table_or(
glm_model_results,
conf_level = 0.95,
output = "tibble",
confint_fast_estimate = FALSE
)
Arguments
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'. |
confint_fast_estimate |
Boolean (default = |
Details
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.
Value
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".
Examples
# 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')