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

View source: R/epiR-tidiers.R

tidy.epi.2by2R Documentation

Tidy a(n) epi.2by2 object


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.


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



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


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.


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.


A tibble::tibble() with columns:


Upper bound on the confidence interval for the estimate.


Lower bound on the confidence interval for the estimate.


Degrees of freedom used by this term in the model.


The two-sided p-value associated with the observed statistic.


The value of a T-statistic to use in a hypothesis that the regression term is non-zero.


The name of the regression term.


Estimated measure of association

See Also

tidy(), epiR::epi.2by2()


# load libraries for models and data

# 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")

broom documentation built on Aug. 30, 2022, 1:07 a.m.