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: 3
Encoding: UTF-8
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-08-19 08:01:19 UTC; wsx
Author: Shixiang Wang ORCID iD [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)

See Also

Useful links:

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

Public 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

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()