增强现实AR>> ezcox>> 返回
项目作者： ShixiangWang

项目描述 ：
Easily Process a Batch of Cox Models
高级语言： R
项目地址: git://github.com/ShixiangWang/ezcox.git
创建时间： 2019-08-06T09:55:11Z
项目社区：https://github.com/ShixiangWang/ezcox
开源协议：GNU General Public License v3.0
下载


ezcox: Easily Process a Batch of Cox Models

CRAN status

Hits
R-CMD-check
Lifecycle: stable

The goal of ezcox is to operate a batch of univariate or multivariate
Cox models and return tidy result.

:arrow_double_down: Installation

You can install the released version of ezcox from
CRAN with:

  1. install.packages("ezcox")

And the development version from GitHub with:

  1. # install.packages("remotes")
  2. remotes::install_github("ShixiangWang/ezcox")

It is possible to install ezcox from Conda conda-forge channel:

  1. conda install r-ezcox --channel conda-forge

Visualization feature of ezcox needs the recent version of
forestmodel, please run the following commands:

  1. remotes::install_github("ShixiangWang/forestmodel")

:beginner: Example

This is a basic example which shows you how to get result from a batch
of cox models.

  1. library(ezcox)
  2. #> Welcome to 'ezcox' package!
  3. #> =======================================================================
  4. #> You are using ezcox version 1.0.2
  5. #> 
  6. #> Project home : https://github.com/ShixiangWang/ezcox
  7. #> Documentation: https://shixiangwang.github.io/ezcox
  8. #> Cite as      : arXiv:2110.14232
  9. #> =======================================================================
  10. #> 
  11. library(survival)
  13. # Build unvariable models
  14. ezcox(lung, covariates = c("age", "sex", "ph.ecog"))
  15. #> => Processing variable age
  16. #> ==> Building Surv object...
  17. #> ==> Building Cox model...
  18. #> ==> Done.
  19. #> => Processing variable sex
  20. #> ==> Building Surv object...
  21. #> ==> Building Cox model...
  22. #> ==> Done.
  23. #> => Processing variable ph.ecog
  24. #> ==> Building Surv object...
  25. #> ==> Building Cox model...
  26. #> ==> Done.
  27. #> # A tibble: 3 × 12
  28. #>   Variable is_cont…¹ contr…² ref_l…³ n_con…⁴ n_ref    beta    HR lower…⁵ upper…⁶
  29. #>   <chr>    <lgl>     <chr>   <chr>     <int> <int>   <dbl> <dbl>   <dbl>   <dbl>
  30. #> 1 age      FALSE     age     age         228   228  0.0187 1.02    1       1.04 
  31. #> 2 sex      FALSE     sex     sex         228   228 -0.531  0.588   0.424   0.816
  32. #> 3 ph.ecog  FALSE     ph.ecog ph.ecog     227   227  0.476  1.61    1.29    2.01 
  33. #> # … with 2 more variables: p.value <dbl>, global.pval <dbl>, and abbreviated
  34. #> #   variable names ¹​is_control, ²​contrast_level, ³​ref_level, ⁴​n_contrast,
  35. #> #   ⁵​lower_95, ⁶​upper_95
  37. # Build multi-variable models
  38. # Control variable 'age'
  39. ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age")
  40. #> => Processing variable sex
  41. #> ==> Building Surv object...
  42. #> ==> Building Cox model...
  43. #> ==> Done.
  44. #> => Processing variable ph.ecog
  45. #> ==> Building Surv object...
  46. #> ==> Building Cox model...
  47. #> ==> Done.
  48. #> # A tibble: 4 × 12
  49. #>   Variable is_cont…¹ contr…² ref_l…³ n_con…⁴ n_ref    beta    HR lower…⁵ upper…⁶
  50. #>   <chr>    <lgl>     <chr>   <chr>     <int> <int>   <dbl> <dbl>   <dbl>   <dbl>
  51. #> 1 sex      FALSE     sex     sex         228   228 -0.513  0.599   0.431   0.831
  52. #> 2 sex      TRUE      age     age         228   228  0.017  1.02    0.999   1.04 
  53. #> 3 ph.ecog  FALSE     ph.ecog ph.ecog     227   227  0.443  1.56    1.24    1.96 
  54. #> 4 ph.ecog  TRUE      age     age         228   228  0.0113 1.01    0.993   1.03 
  55. #> # … with 2 more variables: p.value <dbl>, global.pval <dbl>, and abbreviated
  56. #> #   variable names ¹​is_control, ²​contrast_level, ³​ref_level, ⁴​n_contrast,
  57. #> #   ⁵​lower_95, ⁶​upper_95
  1. lung$ph.ecog = factor(lung$ph.ecog)
  2. zz = ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age", return_models=TRUE)
  3. #> => Processing variable sex
  4. #> ==> Building Surv object...
  5. #> ==> Building Cox model...
  6. #> ==> Done.
  7. #> => Processing variable ph.ecog
  8. #> ==> Building Surv object...
  9. #> ==> Building Cox model...
  10. #> ==> Done.
  11. mds = get_models(zz)
  12. str(mds, max.level = 1)
  13. #> List of 2
  14. #>  $ Surv ~ sex + age    :List of 19
  15. #>   ..- attr(*, "class")= chr "coxph"
  16. #>   ..- attr(*, "Variable")= chr "sex"
  17. #>  $ Surv ~ ph.ecog + age:List of 22
  18. #>   ..- attr(*, "class")= chr "coxph"
  19. #>   ..- attr(*, "Variable")= chr "ph.ecog"
  20. #>  - attr(*, "class")= chr [1:2] "ezcox_models" "list"
  21. #>  - attr(*, "has_control")= logi TRUE
  23. show_models(mds)

:star2: Vignettes

:page_with_curl: Citation

If you are using it in academic research, please cite the preprint
arXiv:2110.14232 along with URL of
this repo.