The goal of regport is to provides R6 classes, methods and utilities to construct, analyze, summarize, and visualize regression models (CoxPH and GLMs).
This package is been superseded by bregr.
You can install the development version of regport like so:
remotes::install_github("ShixiangWang/regport")This is a basic example which shows you how to build and visualize a Cox model.
Prepare data:
library(regport)
library(survival)
lung = survival::lung
lung$sex = factor(lung$sex)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()
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))
covr::package_coverage()
#> regport Coverage: 85.28%
#> R/utils.R: 75.00%
#> R/REGModelList.R: 81.91%
#> R/REGModel.R: 87.56%(MIT) Copyright (c) 2022 Shixiang Wang