Nothing
tests/testthat/_snaps/tbl_regression.md
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables
glm: logistic and poisson regression
Code
tbl_regression(mod_logistic) %>% as.data.frame()
Output
**Characteristic** **log(OR)** **95% CI** **p-value**
1 Age 0.02 0.00, 0.04 0.091
2 T Stage <NA> <NA> <NA>
3 T1 <NA> <NA> <NA>
4 T2 -0.54 -1.4, 0.31 0.2
5 T3 -0.06 -0.95, 0.82 0.9
6 T4 -0.23 -1.1, 0.64 0.6
Code
tbl_regression(mod_poisson, show_single_row = "trt", estimate_fun = purrr::partial(
style_ratio, digits = 1)) %>% as_tibble()
Output
# A tibble: 2 x 4
`**Characteristic**` `**log(IRR)**` `**95% CI**` `**p-value**`
<chr> <chr> <chr> <chr>
1 Age 0.0 0.0, 0.0 0.6
2 Chemotherapy Treatment 0.0 -0.1, 0.2 0.7
Code
tbl_regression(mod_logistic, exponentiate = TRUE, estimate_fun = purrr::partial(
style_ratio, digits = 1)) %>% as.data.frame()
Output
**Characteristic** **OR** **95% CI** **p-value**
1 Age 1.0 1.0, 1.0 0.091
2 T Stage <NA> <NA> <NA>
3 T1 <NA> <NA> <NA>
4 T2 0.6 0.2, 1.4 0.2
5 T3 0.9 0.4, 2.3 0.9
6 T4 0.8 0.3, 1.9 0.6
Code
tbl_regression(mod_poisson, exponentiate = TRUE, show_single_row = "trt",
estimate_fun = purrr::partial(style_ratio, digits = 1)) %>% as_tibble()
Output
# A tibble: 2 x 4
`**Characteristic**` `**IRR**` `**95% CI**` `**p-value**`
<chr> <chr> <chr> <chr>
1 Age 1.0 1.0, 1.0 0.6
2 Chemotherapy Treatment 1.0 0.9, 1.2 0.7
lm: no errors/warnings with standard use
Code
tbl_regression(mod_lm) %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI** **p-value**
1 am -33 -83, 16 0.2
lm with tidyfun: no errors/warnings with standard use
Code
tbl_regression(mod_lm, tidy_fun = broom::tidy) %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI** **p-value**
1 am -33 -83, 16 0.2
survreg: no errors/warnings with standard use
Code
tbl_regression(mod_survreg) %>% as.data.frame()
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
lmer: no errors/warnings with standard use
Code
tbl_regression(mod_lmer) %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI**
1 Days 10 7.4, 13
glmer: no errors/warnings with standard use
Code
tbl_regression(mod_glmer) %>% as.data.frame()
Output
**Characteristic** **log(OR)** **95% CI** **p-value**
1 hp 0.00 -0.07, 0.07 >0.9
2 factor(cyl) <NA> <NA> <NA>
3 4 <NA> <NA> <NA>
4 6 -1.3 -5.1, 2.5 0.5
5 8 -2.1 -14, 9.7 0.7
lm with interactions: no errors/warnings with standard use
Code
tbl_regression(mod_lm_interaction) %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI**
1 Chemotherapy Treatment <NA> <NA>
2 Drug A <NA> <NA>
3 Drug B -0.61 -9.4, 8.2
4 Grade <NA> <NA>
5 I <NA> <NA>
6 II 0.14 -8.3, 8.6
7 III 4.5 -4.9, 14
8 Tumor Response 4.8 -6.9, 16
9 Chemotherapy Treatment * Grade <NA> <NA>
10 Drug B * II 4.2 -8.4, 17
11 Drug B * III -2.9 -16, 9.9
12 Chemotherapy Treatment * Tumor Response <NA> <NA>
13 Drug B * Tumor Response 1.3 -14, 17
14 Grade * Tumor Response <NA> <NA>
15 II * Tumor Response -4.4 -21, 13
16 III * Tumor Response -0.56 -17, 16
17 Chemotherapy Treatment * Grade * Tumor Response <NA> <NA>
18 Drug B * II * Tumor Response 1.3 -22, 24
19 Drug B * III * Tumor Response -5.3 -28, 17
**p-value**
1 <NA>
2 <NA>
3 0.9
4 <NA>
5 <NA>
6 >0.9
7 0.3
8 0.4
9 <NA>
10 0.5
11 0.7
12 <NA>
13 0.9
14 <NA>
15 0.6
16 >0.9
17 <NA>
18 >0.9
19 0.6
Testing lme4 results
Code
tbl_lme4 %>% as.data.frame()
Output
**Characteristic** **OR** **90% CI** **p-value**
1 hp 0.97 0.93, 1.00 0.15
2 factor(vs) <NA> <NA> <NA>
3 0 <NA> <NA> <NA>
4 1 0.01 0.00, 12.4 0.3
Interaction modifications
Code
tbl_i %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI** **p-value**
1 factor(response) <NA> <NA> <NA>
2 0 <NA> <NA> <NA>
3 1 9.1 2.1, 16 0.011
4 Marker Level (ng/mL) 2.0 -1.2, 5.2 0.2
5 Interaction -5.3 -11, -0.11 0.045
tidycrr models work
Code
tbl %>% as.data.frame()
Output
**Characteristic** **HR** **95% CI** **p-value**
1 Age 1.01 0.99, 1.03 0.6
2 Grade <NA> <NA> <NA>
3 I <NA> <NA> <NA>
4 II 1.06 0.52, 2.16 0.9
5 III 1.54 0.82, 2.90 0.2
Code
add_global_p(tbl) %>% as.data.frame()
Output
**Characteristic** **HR** **95% CI** **p-value**
1 Age 1.01 0.99, 1.03 0.6
2 Grade <NA> <NA> 0.3
3 I <NA> <NA> <NA>
4 II 1.06 0.52, 2.16 <NA>
5 III 1.54 0.82, 2.90 <NA>
NSE args could be passed to tidy_plus_plus()
Code
res %>% as.data.frame()
Output
**Characteristic** **OR** **95% CI** **p-value**
1 Age 1.02 1.00, 1.04 0.092
2 Chemotherapy Treatment <NA> <NA> <NA>
3 Drug A <NA> <NA> <NA>
4 Drug B 1.15 0.61, 2.18 0.7
5 T Stage <NA> <NA> <NA>
6 T2 0.56 0.23, 1.33 0.2
7 T3 0.90 0.37, 2.21 0.8
8 T4 0.76 0.31, 1.85 0.6
9 Grade <NA> <NA> <NA>
10 II 0.84 0.38, 1.85 0.7
11 III 1.04 0.48, 2.23 >0.9
add_header_rows = FALSE could be passed to tidy_plus_plus()
Code
res %>% as.data.frame()
Output
**Characteristic** **log(OR)** **95% CI** **p-value**
1 Age 0.02 0.00, 0.04 0.092
2 Drug A <NA> <NA> <NA>
3 Drug B 0.14 -0.49, 0.78 0.7
4 T1 <NA> <NA> <NA>
5 T2 -0.57 -1.5, 0.29 0.2
6 T3 -0.10 -1.0, 0.79 0.8
7 T4 -0.27 -1.2, 0.62 0.6
8 I <NA> <NA> <NA>
9 II -0.17 -0.97, 0.61 0.7
10 III 0.04 -0.73, 0.80 >0.9
Try the gtsummary package in your browser
Any scripts or data that you put into this service are public.
gtsummary documentation built on July 26, 2023, 5:27 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
glm: logistic and poisson regression
Code
tbl_regression(mod_logistic) %>% as.data.frame()
Output
**Characteristic** **log(OR)** **95% CI** **p-value**
1 Age 0.02 0.00, 0.04 0.091
2 T Stage <NA> <NA> <NA>
3 T1 <NA> <NA> <NA>
4 T2 -0.54 -1.4, 0.31 0.2
5 T3 -0.06 -0.95, 0.82 0.9
6 T4 -0.23 -1.1, 0.64 0.6
Code
tbl_regression(mod_poisson, show_single_row = "trt", estimate_fun = purrr::partial(
style_ratio, digits = 1)) %>% as_tibble()
Output
# A tibble: 2 x 4
`**Characteristic**` `**log(IRR)**` `**95% CI**` `**p-value**`
<chr> <chr> <chr> <chr>
1 Age 0.0 0.0, 0.0 0.6
2 Chemotherapy Treatment 0.0 -0.1, 0.2 0.7
Code
tbl_regression(mod_logistic, exponentiate = TRUE, estimate_fun = purrr::partial(
style_ratio, digits = 1)) %>% as.data.frame()
Output
**Characteristic** **OR** **95% CI** **p-value**
1 Age 1.0 1.0, 1.0 0.091
2 T Stage <NA> <NA> <NA>
3 T1 <NA> <NA> <NA>
4 T2 0.6 0.2, 1.4 0.2
5 T3 0.9 0.4, 2.3 0.9
6 T4 0.8 0.3, 1.9 0.6
Code
tbl_regression(mod_poisson, exponentiate = TRUE, show_single_row = "trt",
estimate_fun = purrr::partial(style_ratio, digits = 1)) %>% as_tibble()
Output
# A tibble: 2 x 4
`**Characteristic**` `**IRR**` `**95% CI**` `**p-value**`
<chr> <chr> <chr> <chr>
1 Age 1.0 1.0, 1.0 0.6
2 Chemotherapy Treatment 1.0 0.9, 1.2 0.7
lm: no errors/warnings with standard use
Code
tbl_regression(mod_lm) %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI** **p-value**
1 am -33 -83, 16 0.2
lm with tidyfun: no errors/warnings with standard use
Code
tbl_regression(mod_lm, tidy_fun = broom::tidy) %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI** **p-value**
1 am -33 -83, 16 0.2
survreg: no errors/warnings with standard use
Code
tbl_regression(mod_survreg) %>% as.data.frame()
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
lmer: no errors/warnings with standard use
Code
tbl_regression(mod_lmer) %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI**
1 Days 10 7.4, 13
glmer: no errors/warnings with standard use
Code
tbl_regression(mod_glmer) %>% as.data.frame()
Output
**Characteristic** **log(OR)** **95% CI** **p-value**
1 hp 0.00 -0.07, 0.07 >0.9
2 factor(cyl) <NA> <NA> <NA>
3 4 <NA> <NA> <NA>
4 6 -1.3 -5.1, 2.5 0.5
5 8 -2.1 -14, 9.7 0.7
lm with interactions: no errors/warnings with standard use
Code
tbl_regression(mod_lm_interaction) %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI**
1 Chemotherapy Treatment <NA> <NA>
2 Drug A <NA> <NA>
3 Drug B -0.61 -9.4, 8.2
4 Grade <NA> <NA>
5 I <NA> <NA>
6 II 0.14 -8.3, 8.6
7 III 4.5 -4.9, 14
8 Tumor Response 4.8 -6.9, 16
9 Chemotherapy Treatment * Grade <NA> <NA>
10 Drug B * II 4.2 -8.4, 17
11 Drug B * III -2.9 -16, 9.9
12 Chemotherapy Treatment * Tumor Response <NA> <NA>
13 Drug B * Tumor Response 1.3 -14, 17
14 Grade * Tumor Response <NA> <NA>
15 II * Tumor Response -4.4 -21, 13
16 III * Tumor Response -0.56 -17, 16
17 Chemotherapy Treatment * Grade * Tumor Response <NA> <NA>
18 Drug B * II * Tumor Response 1.3 -22, 24
19 Drug B * III * Tumor Response -5.3 -28, 17
**p-value**
1 <NA>
2 <NA>
3 0.9
4 <NA>
5 <NA>
6 >0.9
7 0.3
8 0.4
9 <NA>
10 0.5
11 0.7
12 <NA>
13 0.9
14 <NA>
15 0.6
16 >0.9
17 <NA>
18 >0.9
19 0.6
Testing lme4 results
Code
tbl_lme4 %>% as.data.frame()
Output
**Characteristic** **OR** **90% CI** **p-value**
1 hp 0.97 0.93, 1.00 0.15
2 factor(vs) <NA> <NA> <NA>
3 0 <NA> <NA> <NA>
4 1 0.01 0.00, 12.4 0.3
Interaction modifications
Code
tbl_i %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI** **p-value**
1 factor(response) <NA> <NA> <NA>
2 0 <NA> <NA> <NA>
3 1 9.1 2.1, 16 0.011
4 Marker Level (ng/mL) 2.0 -1.2, 5.2 0.2
5 Interaction -5.3 -11, -0.11 0.045
tidycrr models work
Code
tbl %>% as.data.frame()
Output
**Characteristic** **HR** **95% CI** **p-value**
1 Age 1.01 0.99, 1.03 0.6
2 Grade <NA> <NA> <NA>
3 I <NA> <NA> <NA>
4 II 1.06 0.52, 2.16 0.9
5 III 1.54 0.82, 2.90 0.2
Code
add_global_p(tbl) %>% as.data.frame()
Output
**Characteristic** **HR** **95% CI** **p-value**
1 Age 1.01 0.99, 1.03 0.6
2 Grade <NA> <NA> 0.3
3 I <NA> <NA> <NA>
4 II 1.06 0.52, 2.16 <NA>
5 III 1.54 0.82, 2.90 <NA>
NSE args could be passed to tidy_plus_plus()
Code
res %>% as.data.frame()
Output
**Characteristic** **OR** **95% CI** **p-value**
1 Age 1.02 1.00, 1.04 0.092
2 Chemotherapy Treatment <NA> <NA> <NA>
3 Drug A <NA> <NA> <NA>
4 Drug B 1.15 0.61, 2.18 0.7
5 T Stage <NA> <NA> <NA>
6 T2 0.56 0.23, 1.33 0.2
7 T3 0.90 0.37, 2.21 0.8
8 T4 0.76 0.31, 1.85 0.6
9 Grade <NA> <NA> <NA>
10 II 0.84 0.38, 1.85 0.7
11 III 1.04 0.48, 2.23 >0.9
add_header_rows = FALSE could be passed to tidy_plus_plus()
Code
res %>% as.data.frame()
Output
**Characteristic** **log(OR)** **95% CI** **p-value**
1 Age 0.02 0.00, 0.04 0.092
2 Drug A <NA> <NA> <NA>
3 Drug B 0.14 -0.49, 0.78 0.7
4 T1 <NA> <NA> <NA>
5 T2 -0.57 -1.5, 0.29 0.2
6 T3 -0.10 -1.0, 0.79 0.8
7 T4 -0.27 -1.2, 0.62 0.6
8 I <NA> <NA> <NA>
9 II -0.17 -0.97, 0.61 0.7
10 III 0.04 -0.73, 0.80 >0.9
Try the gtsummary package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.