The goal of regverse is to provides R6 classes, methods and utilities to construct, analyze, summarize, and visualize regression models (CoxPH and GLMs).
Installation
Install from r-universe:
install.packages("regverse", repos = c("https://wanglabcsu.r-universe.dev", "https://cloud.r-project.org"))Install from GitHub:
remotes::install_github("WangLabCSU/regverse")Simple case
This is a basic example which shows you how to build and visualize a Cox model.
Prepare data:
Create a model:
model <- REGModel$new(
lung,
recipe = list(
x = c("age", "sex"),
y = c("time", "status")
)
)
model
#> <REGModel> ==========
#>
#> Parameter | Coefficient | SE | 95% CI | z | p
#> -----------------------------------------------------------------
#> age | 1.02 | 9.38e-03 | [1.00, 1.04] | 1.85 | 0.065
#> sex [2] | 0.60 | 0.10 | [0.43, 0.83] | -3.06 | 0.002
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a Wald z-distribution approximation.
#> [coxph] model ==========You can also create it with formula:
model <- REGModel$new(
lung,
recipe = Surv(time, status) ~ age + sex
)
model
#> <REGModel> ==========
#>
#> Parameter | Coefficient | SE | 95% CI | z | p
#> -----------------------------------------------------------------
#> age | 1.02 | 9.38e-03 | [1.00, 1.04] | 1.85 | 0.065
#> sex [2] | 0.60 | 0.10 | [0.43, 0.83] | -3.06 | 0.002
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a Wald z-distribution approximation.
#> [coxph] model ==========Take a look at the model result (package see is required):
model$plot()
Visualize with more nice forest plot.
model$get_forest_data()
model$plot_forest()
Batch processing models
For building a list of regression model, unlike above, a lazy building approach is used, i.e., $build() must manually typed after creating REGModelList object. (This also means you can check or modify the setting before building if necessary)
ml <- REGModelList$new(
data = mtcars,
y = "mpg",
x = c("factor(cyl)", colnames(mtcars)[3:5]),
covars = c(colnames(mtcars)[8:9], "factor(gear)")
)
ml
#> <REGModelList> ==========
#>
#> X(s): factor(cyl), disp, hp, drat
#> Y(s): mpg
#> covars: vs, am, factor(gear)
#>
#> Not build yet, run $build() method
#> [] model ==========
ml$build(f = "gaussian")
str(ml$result)
#> Classes 'data.table' and 'data.frame': 25 obs. of 10 variables:
#> $ focal_term: chr "factor(cyl)" "factor(cyl)" "factor(cyl)" "factor(cyl)" ...
#> $ variable : chr "(Intercept)" "factor(cyl)6" "factor(cyl)8" "vs" ...
#> $ estimate : num 23.28 -5.34 -8.5 1.68 4.31 ...
#> $ SE : num 3.1 1.89 3.05 2.35 2.16 ...
#> $ CI : num 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 ...
#> $ CI_low : num 17.203 -9.04 -14.473 -2.931 0.084 ...
#> $ CI_high : num 29.37 -1.64 -2.53 6.3 8.54 ...
#> $ t : num 7.504 -2.829 -2.791 0.715 1.999 ...
#> $ df_error : int 25 25 25 25 25 25 25 26 26 26 ...
#> $ p : num 6.18e-14 4.67e-03 5.25e-03 4.75e-01 4.56e-02 ...
#> - attr(*, ".internal.selfref")=<externalptr>
str(ml$forest_data)
#> Classes 'data.table' and 'data.frame': 6 obs. of 17 variables:
#> $ focal_term: chr "factor(cyl)" "factor(cyl)" "factor(cyl)" "disp" ...
#> $ variable : chr "factor(cyl)" NA NA "disp" ...
#> $ term : chr "factor(cyl)4" "factor(cyl)6" "factor(cyl)8" "disp" ...
#> $ term_label: chr "factor(cyl)" "factor(cyl)" "factor(cyl)" "disp" ...
#> $ class : chr "factor" "factor" "factor" "numeric" ...
#> $ level : chr "4" "6" "8" NA ...
#> $ level_no : int 1 2 3 NA NA NA
#> $ n : int 11 7 14 32 32 32
#> $ estimate : num 0 -5.3404 -8.5026 -0.0282 -0.0515 ...
#> $ SE : num NA 1.88767 3.04626 0.00924 0.01201 ...
#> $ CI : num NA 0.95 0.95 0.95 0.95 0.95
#> $ CI_low : num NA -9.0402 -14.4732 -0.0463 -0.075 ...
#> $ CI_high : num NA -1.6407 -2.532 -0.0101 -0.0279 ...
#> $ t : num NA -2.83 -2.79 -3.05 -4.28 ...
#> $ df_error : int NA 25 25 26 26 26
#> $ p : num NA 4.67e-03 5.25e-03 2.27e-03 1.84e-05 ...
#> $ reference : logi TRUE FALSE FALSE FALSE FALSE FALSE
#> - attr(*, ".internal.selfref")=<externalptr>
ml$plot_forest(ref_line = 0, xlim = c(-15, 8))