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"), ...)

## Arguments

x 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. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

## 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.

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

## 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

## Examples


library(epiR)
#> Package epiR 2.0.33 is loaded#> Type help(epi.about) for summary information#> Type browseVignettes(package = 'epiR') to learn how to use epiR for applied epidemiological analyses#>
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")
#> # A tibble: 15 × 4
#>    term                estimate conf.low conf.high
#>    <chr>                  <dbl>    <dbl>     <dbl>
#>  1 PR.strata.wald         4.01    1.43      11.2
#>  2 PR.strata.taylor       4.01    1.43      11.2
#>  3 PR.strata.score        4.01    1.49      10.8
#>  4 OR.strata.wald         4.03    1.43      11.3
#>  5 OR.strata.cfield       4.03   NA         NA
#>  6 OR.strata.score        4.03    1.49      10.9
#>  7 OR.strata.mle          4.02    1.34      14.4
#>  8 ARisk.strata.wald      0.448   0.0992     0.797
#>  9 ARisk.strata.score     0.448   0.142      0.882
#> 10 NNT.strata.wald      223.    125.      1008.
#> 11 NNT.strata.score     223.    113.       705.
#> 12 PARisk.strata.wald     0.176  -0.0225     0.375
#> 13 PARisk.strata.piri     0.176   0.0389     0.314
#> 14 AFRisk.strata.wald     0.750   0.301      0.911
#> 15 PAFRisk.strata.wald    0.542   0.0361     0.782tidy(fit, parameters = "stat")
#> # 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