tests/testthat/_snaps/tbl_regression_methods.md

In ddsjoberg/gtsummary: Presentation-Ready Data Summary and Analytic Result Tables

tbl_regression.model_fit()

Code
  as.data.frame(tbl_regression(parsnip::fit(parsnip::set_mode(parsnip::set_engine(
    parsnip::linear_reg(), "lm"), "regression"), age ~ grade + stage, data = trial)))
Message
  Extracting {parsnip} model fit with `tbl_regression(x = x$fit, ...)`
Output
    **Characteristic** **Beta** **95% CI** **p-value**
  1              Grade     <NA>       <NA>        <NA>
  2                  I     <NA>       <NA>        <NA>
  3                 II      1.6  -3.5, 6.7         0.5
  4                III      2.2  -2.8, 7.3         0.4
  5            T Stage     <NA>       <NA>        <NA>
  6                 T1     <NA>       <NA>        <NA>
  7                 T2      1.4  -4.2, 7.0         0.6
  8                 T3      2.8  -3.2, 8.8         0.4
  9                 T4     -2.0  -7.9, 3.9         0.5

tbl_regression.workflow()

Code
  as.data.frame(tbl_regression(parsnip::fit(workflows::add_formula(workflows::add_model(
    workflows::workflow(), parsnip::set_engine(parsnip::logistic_reg(), "glm")),
  factor(response) ~ age + stage), data = trial)))
Message
  i To take full advantage of model formatting, e.g. grouping categorical variables, please add the following argument to the `workflows::add_model()` call:
  * `blueprint = hardhat::default_formula_blueprint(indicators = 'none')`
  Extracting {parsnip} model fit with `tbl_regression(x = x$fit, ...)`
Output
    **Characteristic** **log(OR)**  **95% CI** **p-value**
  1                age        0.02  0.00, 0.04       0.091
  2            stageT2       -0.54  -1.4, 0.31         0.2
  3            stageT3       -0.06 -0.95, 0.82         0.9
  4            stageT4       -0.23  -1.1, 0.64         0.6

tbl_regression.survreg()

Code
  as.data.frame(tbl_regression(survival::survreg(survival::Surv(time, status) ~
    age + ph.ecog, data = survival::lung)))
Condition
  Warning:
  The `exponentiate` argument is not supported in the `tidy()` method for `survreg` objects and will be ignored.
Output
    **Characteristic** **Beta**   **95% CI** **p-value**
  1                age    -0.01  -0.02, 0.01         0.3
  2            ph.ecog    -0.33 -0.49, -0.16      <0.001

tbl_regression.mira()

Code
  as.data.frame(tbl_regression(with(suppressWarnings(mice::mice(trial, m = 2)),
  lm(age ~ marker + grade))))
Output

   iter imp variable
    1   1  age  marker  response
    1   2  age  marker  response
    2   1  age  marker  response
    2   2  age  marker  response
    3   1  age  marker  response
    3   2  age  marker  response
    4   1  age  marker  response
    4   2  age  marker  response
    5   1  age  marker  response
    5   2  age  marker  response
      **Characteristic** **Beta** **95% CI** **p-value**
  1 Marker Level (ng/mL)     0.24  -2.2, 2.6         0.8
  2                Grade     <NA>       <NA>        <NA>
  3                    I     <NA>       <NA>        <NA>
  4                   II      1.3  -4.5, 7.0         0.7
  5                  III      1.9  -3.3, 7.1         0.5

Code
  as.data.frame(tbl_regression(mice::pool(with(suppressWarnings(mice::mice(trial,
    m = 2)), lm(age ~ marker + grade)))))
Output

   iter imp variable
    1   1  age  marker  response
    1   2  age  marker  response
    2   1  age  marker  response
    2   2  age  marker  response
    3   1  age  marker  response
    3   2  age  marker  response
    4   1  age  marker  response
    4   2  age  marker  response
    5   1  age  marker  response
    5   2  age  marker  response
Message
  i Pass the <mice> model to `tbl_regression()` before models have been combined with `mice::pool()`.
  * The default tidier, `pool_and_tidy_mice()`, will both pool and tidy the regression model.
  * `mice::mice(trial, m = 2) |> with(lm(age ~ marker + grade)) |> tbl_regression()`
Output
  data frame with 0 columns and 0 rows

tbl_regression.lmerMod()

Code
  as.data.frame(tbl_regression(lme4::lmer(mpg ~ hp + (1 | cyl), mtcars)))
Output
    **Characteristic** **Beta**  **95% CI**
  1                 hp    -0.03 -0.06, 0.00

tbl_regression.gam()

Code
  as.data.frame(tbl_regression(gam(mpg ~ s(hp) + factor(cyl), data = mtcars)))
Output
    **Characteristic** **Beta**  **95% CI** **p-value**
  1        factor(cyl)     <NA>        <NA>        <NA>
  2                  4     <NA>        <NA>        <NA>
  3                  6     -4.5 -8.4, -0.68       0.030
  4                  8     -7.8   -14, -1.4       0.026
  5              s(hp)     <NA>        <NA>       0.093

tbl_regression.crr()

Code
  set.seed(10)
  ftime <- rexp(200)
  fstatus <- sample(0:2, 200, replace = TRUE)
  cov <- matrix(runif(600), nrow = 200)
  dimnames(cov)[[2]] <- c("x1", "x2", "x3")
  as.data.frame(tbl_regression(crr(ftime, fstatus, cov)))
Message
  For better summary support, build model with `tidycmprsk::crr()`.
  Visit <https://mskcc-epi-bio.github.io/tidycmprsk/> for details.
  x Unable to identify the list of variables.

  This is usually due to an error calling `stats::model.frame(x)`or `stats::model.matrix(x)`.
  It could be the case if that type of model does not implement these methods.
  Rarely, this error may occur if the model object was created within
  a functional programming framework (e.g. using `lappy()`, `purrr::map()`, etc.).
Output
    **Characteristic** **log(HR)**  **95% CI** **p-value**
  1                 x1        0.27  -0.56, 1.1         0.5
  2                 x2       -0.06 -0.80, 0.69         0.9
  3                 x3        0.28  -0.47, 1.0         0.5

tbl_regression.multinom()

Code
  as.data.frame(tbl_regression(nnet::multinom(cyl ~ am, mtcars)))
Output
  # weights:  9 (4 variable)
  initial  value 35.155593 
  final  value 29.311125 
  converged
Message
  i Multinomial models have a different underlying structure than the models gtsummary was designed for.
  * Functions designed to work with `tbl_regression()` objects may yield unexpected results.
Output
    **Outcome** **Characteristic** **log(OR)**  **95% CI** **p-value**
  1           6                 am        -1.3  -3.3, 0.73         0.2
  2           8                 am        -2.8 -4.8, -0.77       0.007

ddsjoberg/gtsummary documentation built on March 1, 2025, 7:52 p.m.