tidy.epi.2by2: Tidy a(n) epi.2by2 object

Description

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

# S3 method for epi.2by2
tidy(x, parameters = c("moa", "stat"), ...)

Value

A tibble::tibble() with columns:

conf.high: Upper bound on the confidence interval for the estimate.
conf.low: Lower bound on the confidence interval for the estimate.
df: Degrees of freedom used by this term in the model.
p.value: The two-sided p-value associated with the observed statistic.
statistic: The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
term: The name of the regression term.
estimate: Estimated measure of association

Arguments

x

A epi.2by2 object produced by a call to epiR::epi.2by2()

parameters

Return measures of association (moa) or test statistics (stat), default is moa (measures of association)

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

Details

The tibble has a column for each of the measures of association or tests contained in massoc or massoc.detail when epiR::epi.2by2() is called.

Examples

Run this code

if (FALSE) { # rlang::is_installed("epiR")

# load libraries for models and data
library(epiR)

# generate data
dat <- matrix(c(13, 2163, 5, 3349), nrow = 2, byrow = TRUE)

rownames(dat) <- c("DF+", "DF-")
colnames(dat) <- c("FUS+", "FUS-")

# fit model
fit <- epi.2by2(
  dat = as.table(dat), method = "cross.sectional",
  conf.level = 0.95, units = 100, outcome = "as.columns"
)

# summarize model fit with tidiers
tidy(fit, parameters = "moa")
tidy(fit, parameters = "stat")
}