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 epi.2by2 tidy(x, parameters = c("moa", "stat"), ...)
x | A |
---|---|
parameters | Return measures of association ( |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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
if (requireNamespace("epiR", quietly = TRUE)) { library(epiR) dat <- matrix(c(13, 2163, 5, 3349), nrow = 2, byrow = TRUE) rownames(dat) <- c("DF+", "DF-") colnames(dat) <- c("FUS+", "FUS-") fit <- epi.2by2( dat = as.table(dat), method = "cross.sectional", conf.level = 0.95, units = 100, outcome = "as.columns" ) tidy(fit, parameters = "moa") tidy(fit, parameters = "stat") } #> Package epiR 2.0.39 is loaded #> Type help(epi.about) for summary information #> Type browseVignettes(package = 'epiR') to learn how to use epiR for applied epidemiological analyses #> #> # A tibble: 3 × 4 #> term statistic df p.value #> <chr> <dbl> <dbl> <dbl> #> 1 chi2.strata.uncor 8.18 1 0.00424 #> 2 chi2.strata.yates 6.85 1 0.00885 #> 3 chi2.strata.fisher NA NA 0.00635