Code
tbl_regression(mod, tidy_fun = tidy_standardize) %>% as.data.frame()
Message
tidy_standardize(): Estimating standardized coefs with
`parameters::standardize_parameters(model = x, ci = 0.95)`
Output
**Characteristic** **Beta** **95% CI**
1 Marker Level (ng/mL) 0.00 -0.15, 0.15
2 Grade <NA> <NA>
3 I <NA> <NA>
4 II 0.04 -0.32, 0.41
5 III 0.17 -0.20, 0.53
Code
tbl_regression(mod, tidy_fun = tidy_bootstrap) %>% as.data.frame()
Message
tidy_bootstrap(): Estimating bootstrapped coefs with
`parameters::bootstrap_parameters(model = x, ci = 0.95, test = "p")`
Output
**Characteristic** **Beta** **95% CI** **p-value**
1 Marker Level (ng/mL) -0.09 -2.5, 2.3 >0.9
2 Grade <NA> <NA> <NA>
3 I <NA> <NA> <NA>
4 II 0.67 -4.6, 6.1 0.8
5 III 2.5 -3.1, 7.3 0.4
Code
tbl_mice %>% as.data.frame()
Output
**Characteristic** **log(OR)** **95% CI** **p-value**
1 Age 0.02 0.00, 0.04 0.062
2 Marker Level (ng/mL) 0.33 -0.02, 0.68 0.065
3 Grade <NA> <NA> <NA>
4 I <NA> <NA> <NA>
5 II 0.12 -0.72, 0.96 0.8
6 III 0.11 -0.66, 0.87 0.8
Code
tbl_nnet %>% as.data.frame()
Output
**Outcome** **Characteristic** **log(OR)** **95% CI** **p-value**
1 II Age 0.0 0.0, 0.0 0.6
2 III Age 0.0 0.0, 0.0 0.4
Code
tbl_nnet %>% as_tibble()
Output
# A tibble: 2 x 5
`**Outcome**` `**Characteristic**` `**log(OR)**` `**95% CI**` `**p-value**`
<chr> <chr> <chr> <chr> <chr>
1 II Age 0.0 0.0, 0.0 0.6
2 III Age 0.0 0.0, 0.0 0.4
Code
mod %>% tidy_gam()
Output
# A tibble: 4 x 8
term estimate std.error statistic p.value edf ref.df parametric
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
1 (Intercept) -0.821 0.279 -2.95 0.00320 NA NA TRUE
2 gradeII 0.0406 0.417 0.0975 0.922 NA NA TRUE
3 gradeIII -0.0179 0.400 -0.0447 0.964 NA NA TRUE
4 s(marker,age) NA NA 4.63 0.0987 2.00 2.00 FALSE
Code
mod %>% tbl_regression(exponentiate = TRUE, label = `s(marker,age)` ~
"Smoothed marker/age") %>% as.data.frame()
Output
**Characteristic** **OR** **95% CI** **p-value**
1 Grade <NA> <NA> <NA>
2 I <NA> <NA> <NA>
3 II 1.04 0.46, 2.36 >0.9
4 III 0.98 0.45, 2.15 >0.9
5 Smoothed marker/age <NA> <NA> 0.10
Code
glm(response ~ age + trt, trial, family = binomial) %>% tbl_regression(
tidy_fun = purrr::partial(tidy_robust, vcov_estimation = "CL"), exponentiate = TRUE) %>%
as.data.frame()
Message
Arguments `vcov` and `vcov_args` have not been specified in `tidy_robust()`. Specify at least one to obtain robust standard errors.
tidy_robust(): Robust estimation with
`parameters::model_parameters(model = x, ci = 0.95, vcov_estimation = "CL")`
Output
**Characteristic** **OR** **95% CI** **p-value**
1 Age 1.02 1.00, 1.04 0.095
2 Chemotherapy Treatment <NA> <NA> <NA>
3 Drug A <NA> <NA> <NA>
4 Drug B 1.13 0.60, 2.13 0.7
Code
glm(response ~ age + trt, trial, family = binomial) %>% tidy_robust()
Message
Arguments `vcov` and `vcov_args` have not been specified in `tidy_robust()`. Specify at least one to obtain robust standard errors.
tidy_robust(): Robust estimation with
`parameters::model_parameters(model = x, ci = 0.95)`
Output
term estimate std.error conf.level conf.low conf.high
1 (Intercept) -1.7424131 0.60136068 0.95 -2.966150321 -0.59785416
2 age 0.0189970 0.01137703 0.95 -0.002978589 0.04182663
3 trtDrug B 0.1254909 0.32080236 0.95 -0.502963841 0.75829003
statistic df.error p.value
1 -2.8974509 Inf 0.003762086
2 1.6697681 Inf 0.094965255
3 0.3911783 Inf 0.695665447
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