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Title:

Regression Model Processing Port

Version:

0.3.1

Description:

Provides R6 classes, methods and utilities to construct, analyze, summarize, and visualize regression models.

License:

MIT + file LICENSE

URL:

https://github.com/ShixiangWang/regport, https://shixiangwang.github.io/regport/

BugReports:

https://github.com/ShixiangWang/regport/issues

Imports:

broom.helpers, data.table, dplyr, forestploter, glue, parameters, R6, rlang (≥ 0.4.11), stats, survival

Suggests:

parallel, roxygen2, see, testthat (≥ 3.0.0)

Config/testthat/edition:

Encoding:

UTF-8

RoxygenNote:

7.3.2

NeedsCompilation:

Packaged:

2025-08-19 08:01:19 UTC; wsx

Author:

Shixiang Wang

[aut, cre]

Maintainer:

Shixiang Wang <w_shixiang@163.com>

Repository:

CRAN

Date/Publication:

2025-08-19 09:00:02 UTC

regport: Regression Model Processing Port

Description

Provides R6 classes, methods and utilities to construct, analyze, summarize, and visualize regression models.

Author(s)

Maintainer: Shixiang Wang w_shixiang@163.com (ORCID)

R6 class representing a regression model

Description

Contains fields storing data and methods to build, process and visualize a regression model. Currently, this class is designed for CoxPH and GLM regression models.

Public fields

data: a data.table storing modeling data.
recipe: an R formula storing model formula.
terms: all terms (covariables, i.e. columns) used for building model.
args: other arguments used for building model.
model: a constructed model.
type: model type (class).
result: model result, a object of parameters_model. Can be converted into data.frame with as.data.frame() or data.table::as.data.table().
forest_data: more detailed data used for plotting forest.

Methods

Method `new()`

Build a REGModel object.

Usage

REGModel$new(
  data,
  recipe,
  ...,
  f = c("coxph", "binomial", "gaussian", "Gamma", "inverse.gaussian", "poisson", "quasi",
    "quasibinomial", "quasipoisson"),
  exp = NULL,
  ci = 0.95
)

Arguments

data: a data.table storing modeling data.
recipe: an R formula or a list with two elements 'x' and 'y', where 'x' is for covariables and 'y' is for label. See example for detail operation.
...: other parameters passing to corresponding regression model function.
f: a length-1 string specifying modeling function or family of glm(), default is 'coxph'. Other options are members of GLM family, see stats::family(). 'binomial' is logistic, and 'gaussian' is linear.
exp: logical, indicating whether or not to exponentiate the the coefficients.
ci: confidence Interval (CI) level. Default to 0.95 (95%). e.g. survival::coxph().

Returns

a REGModel R6 object.

Method `get_forest_data()`

get tidy data for plotting forest.

Usage

REGModel$get_forest_data(separate_factor = FALSE, global_p = FALSE)

Arguments

separate_factor: separate factor/class as a blank row.
global_p: if TRUE, return global p value.

Method `plot_forest()`

plot forest.

Usage

REGModel$plot_forest(ref_line = NULL, xlim = NULL, ...)

Arguments

ref_line: reference line, default is 1 for HR.
xlim: limits of x axis.
...: other plot options passing to forestploter::forest(). Also check https://github.com/adayim/forestploter to see more complex adjustment of the result plot.

Method `plot()`

print the REGModel$result with default plot methods from see package.

Usage

REGModel$plot(...)

Arguments

...: other parameters passing to plot() in see:::plot.see_parameters_model function.

Method `print()`

print the REGModel object

Usage

REGModel$print(...)

Arguments

...: unused.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

REGModel$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

library(survival)
test1 <- data.frame(
  time = c(4, 3, 1, 1, 2, 2, 3),
  status = c(1, 1, 1, 0, 1, 1, 0),
  x = c(0, 2, 1, 1, 1, 0, 0),
  sex = c(0, 0, 0, 0, 1, 1, 1)
)
test1$sex <- factor(test1$sex)

# --------------
# Build a model
# --------------

# way 1:
mm <- REGModel$new(
  test1,
  Surv(time, status) ~ x + strata(sex)
)
mm
as.data.frame(mm$result)
if (require("see")) mm$plot()
mm$print() # Same as print(mm)

# way 2:
mm2 <- REGModel$new(
  test1,
  recipe = list(
    x = c("x", "strata(sex)"),
    y = c("time", "status")
  )
)
mm2

# Add other parameters, e.g., weights
# For more, see ?coxph
mm3 <- REGModel$new(
  test1,
  recipe = list(
    x = c("x", "strata(sex)"),
    y = c("time", "status")
  ),
  weights = c(1, 1, 1, 2, 2, 2, 3)
)
mm3$args

# ----------------------
# Another type of model
# ----------------------
library(stats)
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
data <- data.frame(treatment, outcome, counts)

mm4 <- REGModel$new(
  data,
  counts ~ outcome + treatment,
  f = "poisson"
)
mm4
mm4$plot_forest()
mm4$get_forest_data()
mm4$plot_forest()

R6 class representing a list of regression model

Description

Contains fields storing data and methods to build, process and visualize a list of regression model. Currently, this class is designed for CoxPH and GLM regression models.

Public fields

data: a data.table storing modeling data.
x: focal variables (terms).
y: predicted variables or expression.
covars: covariables.
mlist: a list of REGModel.
args: other arguments used for building model.
type: model type (class).
result: model result, a object of parameters_model. Can be converted into data.frame with as.data.frame() or data.table::as.data.table().
forest_data: more detailed data used for plotting forest.

Methods

Public methods

REGModelList$new()
REGModelList$build()
REGModelList$plot_forest()
REGModelList$print()
REGModelList$clone()

Method `new()`

Create a REGModelList object.

Usage

REGModelList$new(data, y, x, covars = NULL)

Arguments

data: a data.table storing modeling data.
y: predicted variables or expression.
x: focal variables (terms).
covars: covariables.

Returns

a REGModelList R6 object.

Method `build()`

Build REGModelList object.

Usage

REGModelList$build(
  f = c("coxph", "binomial", "gaussian", "Gamma", "inverse.gaussian", "poisson", "quasi",
    "quasibinomial", "quasipoisson"),
  exp = NULL,
  ci = 0.95,
  parallel = FALSE,
  ...
)

Arguments

f: a length-1 string specifying modeling function or family of glm(), default is 'coxph'. Other options are members of GLM family, see stats::family(). 'binomial' is logistic, and 'gaussian' is linear.
exp: logical, indicating whether or not to exponentiate the the coefficients.
ci: confidence Interval (CI) level. Default to 0.95 (95%). e.g. survival::coxph().
parallel: if TRUE, use N-1 cores to run the task.
...: other parameters passing to corresponding regression model function.

Returns

a REGModel R6 object.

Method `plot_forest()`

plot forest.

Usage

REGModelList$plot_forest(
  ref_line = NULL,
  xlim = NULL,
  vars = NULL,
  p = NULL,
  ...
)

Arguments

ref_line: reference line, default is 1 for HR.
xlim: limits of x axis.
vars: selected variables to show.
p: selected variables with level' pvalue lower than p.
...: other plot options passing to forestploter::forest(). Also check https://github.com/adayim/forestploter to see more complex adjustment of the result plot.

Method `print()`

print the REGModelList object

Usage

REGModelList$print(...)

Arguments

...: unused.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

REGModelList$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

ml <- REGModelList$new(
  data = mtcars,
  y = "mpg",
  x = c("factor(cyl)", colnames(mtcars)[3:5]),
  covars = c(colnames(mtcars)[8:9], "factor(gear)")
)
ml
ml$print()
ml$plot_forest()

ml$build(f = "gaussian")
## Not run: 
ml$build(f = "gaussian", parallel = TRUE)

## End(Not run)
ml$print()
ml$result
ml$forest_data
ml$plot_forest()

regport: Regression Model Processing Port

Description

Author(s)

See Also

R6 class representing a regression model

Description

Public fields

Methods

Public methods

Method new()

Usage

Arguments

Returns

Method get_forest_data()

Usage

Arguments

Method plot_forest()

Usage

Arguments

Method plot()

Usage

Arguments

Method print()

Usage

Arguments

Method clone()

Usage

Arguments

Examples

R6 class representing a list of regression model

Description

Public fields

Methods

Public methods

Method new()

Usage

Arguments

Returns

Method build()

Usage

Arguments

Returns

Method plot_forest()

Usage

Arguments

Method print()

Usage

Arguments

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `get_forest_data()`

Method `plot_forest()`

Method `plot()`

Method `print()`

Method `clone()`

Method `new()`

Method `build()`

Method `plot_forest()`

Method `print()`

Method `clone()`