R6 class representing a list of regression model

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

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
#> <REGModelList>    ========== 
#> 
#> X(s): factor(cyl), disp, hp, drat 
#> Y(s): mpg 
#> covars: vs, am, factor(gear) 
#> 
#> Not build yet, run $build() method 
#> [] model ==========
ml$print()
#> <REGModelList>    ========== 
#> 
#> X(s): factor(cyl), disp, hp, drat 
#> Y(s): mpg 
#> covars: vs, am, factor(gear) 
#> 
#> Not build yet, run $build() method 
#> [] model ==========
ml$plot_forest()
#> Please run $build() before $plot_forest()
#> NULL

ml$build(f = "gaussian")
ml$build(f = "gaussian", parallel = TRUE)
ml$print()
#> <REGModelList>    ========== 
#> 
#> X(s): factor(cyl), disp, hp, drat 
#> Y(s): mpg 
#> covars: vs, am, factor(gear) 
#> ----
#>  Result: 
#>      focal_term      variable    estimate          SE   CI       CI_low
#>  1: factor(cyl)   (Intercept) 23.28425686 3.102848398 0.95  17.20278575
#>  2: factor(cyl)  factor(cyl)6 -5.34044537 1.887666208 0.95  -9.04020315
#>  3: factor(cyl)  factor(cyl)8 -8.50258933 3.046261922 0.95 -14.47315299
#>  4: factor(cyl)            vs  1.68270326 2.353962963 0.95  -2.93097937
#>  5: factor(cyl)            am  4.31056447 2.156460225 0.95   0.08398010
#>  6: factor(cyl) factor(gear)4 -1.24673744 2.264460155 0.95  -5.68499779
#>  7: factor(cyl) factor(gear)5 -2.08223718 2.637768301 0.95  -7.25216805
#>  8:        disp   (Intercept) 24.65009765 3.360529174 0.95  18.06358150
#>  9:        disp          disp -0.02821806 0.009243695 0.95  -0.04633537
#> 10:        disp            vs  3.32060607 1.856416709 0.95  -0.31790382
#> 11:        disp            am  4.67281942 2.094171633 0.95   0.56831844
#> 12:        disp factor(gear)4 -2.52785760 2.211888366 0.95  -6.86307914
#> 13:        disp factor(gear)5 -2.89344598 2.574494892 0.95  -7.93936325
#> 14:          hp   (Intercept) 24.56544525 2.420389406 0.95  19.82156919
#> 15:          hp            hp -0.05145024 0.012010845 0.95  -0.07499106
#> 16:          hp            vs  3.01661776 1.574374865 0.95  -0.06910027
#> 17:          hp            am  5.11093165 1.798753134 0.95   1.58544029
#> 18:          hp factor(gear)4 -1.34845141 1.896221483 0.95  -5.06497722
#> 19:          hp factor(gear)5  1.16396634 2.363608073 0.95  -3.46862036
#> 20:        drat   (Intercept)  5.36259687 7.058661715 0.95  -8.47212587
#> 21:        drat          drat  3.01056864 2.245957350 0.95  -1.39142687
#> 22:        drat            vs  6.56480882 1.648702814 0.95   3.33341068
#> 23:        drat            am  6.04958546 2.304973039 0.95   1.53192132
#> 24:        drat factor(gear)4 -2.50572707 2.796115713 0.95  -7.98601316
#> 25:        drat factor(gear)5 -3.13453090 3.068393855 0.95  -9.14847234
#>      focal_term      variable    estimate          SE   CI       CI_low
#>         CI_high          t df_error            p
#>  1: 29.36572797  7.5041555       25 6.182588e-14
#>  2: -1.64068758 -2.8291259       25 4.667533e-03
#>  3: -2.53202568 -2.7911550       25 5.252031e-03
#>  4:  6.29638589  0.7148385       25 4.747089e-01
#>  5:  8.53714885  1.9989075       25 4.561837e-02
#>  6:  3.19152291 -0.5505672       25 5.819304e-01
#>  7:  3.08769369 -0.7893935       25 4.298820e-01
#>  8: 31.23661380  7.3351834       26 2.214181e-13
#>  9: -0.01010075 -3.0526817       26 2.268064e-03
#> 10:  6.95911596  1.7887180       26 7.366023e-02
#> 11:  8.77732040  2.2313450       26 2.565828e-02
#> 12:  1.80736393 -1.1428504       26 2.531007e-01
#> 13:  2.15247129 -1.1238888       26 2.610602e-01
#> 14: 29.30932132 10.1493773       26 3.334845e-24
#> 15: -0.02790942 -4.2836487       26 1.838531e-05
#> 16:  6.10233579  1.9160734       26 5.535576e-02
#> 17:  8.63642301  2.8413747       26 4.491950e-03
#> 18:  2.36807440 -0.7111255       26 4.770065e-01
#> 19:  5.79655303  0.4924532       26 6.223990e-01
#> 20: 19.19731961  0.7597186       26 4.474228e-01
#> 21:  7.41256416  1.3404389       26 1.801027e-01
#> 22:  9.79620695  3.9818024       26 6.839463e-05
#> 23: 10.56724960  2.6245797       26 8.675598e-03
#> 24:  2.97455903 -0.8961457       26 3.701750e-01
#> 25:  2.87941055 -1.0215543       26 3.069919e-01
#>         CI_high          t df_error            p
#> [glm/lm] model ==========
ml$result
#>      focal_term      variable    estimate          SE   CI       CI_low
#>  1: factor(cyl)   (Intercept) 23.28425686 3.102848398 0.95  17.20278575
#>  2: factor(cyl)  factor(cyl)6 -5.34044537 1.887666208 0.95  -9.04020315
#>  3: factor(cyl)  factor(cyl)8 -8.50258933 3.046261922 0.95 -14.47315299
#>  4: factor(cyl)            vs  1.68270326 2.353962963 0.95  -2.93097937
#>  5: factor(cyl)            am  4.31056447 2.156460225 0.95   0.08398010
#>  6: factor(cyl) factor(gear)4 -1.24673744 2.264460155 0.95  -5.68499779
#>  7: factor(cyl) factor(gear)5 -2.08223718 2.637768301 0.95  -7.25216805
#>  8:        disp   (Intercept) 24.65009765 3.360529174 0.95  18.06358150
#>  9:        disp          disp -0.02821806 0.009243695 0.95  -0.04633537
#> 10:        disp            vs  3.32060607 1.856416709 0.95  -0.31790382
#> 11:        disp            am  4.67281942 2.094171633 0.95   0.56831844
#> 12:        disp factor(gear)4 -2.52785760 2.211888366 0.95  -6.86307914
#> 13:        disp factor(gear)5 -2.89344598 2.574494892 0.95  -7.93936325
#> 14:          hp   (Intercept) 24.56544525 2.420389406 0.95  19.82156919
#> 15:          hp            hp -0.05145024 0.012010845 0.95  -0.07499106
#> 16:          hp            vs  3.01661776 1.574374865 0.95  -0.06910027
#> 17:          hp            am  5.11093165 1.798753134 0.95   1.58544029
#> 18:          hp factor(gear)4 -1.34845141 1.896221483 0.95  -5.06497722
#> 19:          hp factor(gear)5  1.16396634 2.363608073 0.95  -3.46862036
#> 20:        drat   (Intercept)  5.36259687 7.058661715 0.95  -8.47212587
#> 21:        drat          drat  3.01056864 2.245957350 0.95  -1.39142687
#> 22:        drat            vs  6.56480882 1.648702814 0.95   3.33341068
#> 23:        drat            am  6.04958546 2.304973039 0.95   1.53192132
#> 24:        drat factor(gear)4 -2.50572707 2.796115713 0.95  -7.98601316
#> 25:        drat factor(gear)5 -3.13453090 3.068393855 0.95  -9.14847234
#>      focal_term      variable    estimate          SE   CI       CI_low
#>         CI_high          t df_error            p
#>  1: 29.36572797  7.5041555       25 6.182588e-14
#>  2: -1.64068758 -2.8291259       25 4.667533e-03
#>  3: -2.53202568 -2.7911550       25 5.252031e-03
#>  4:  6.29638589  0.7148385       25 4.747089e-01
#>  5:  8.53714885  1.9989075       25 4.561837e-02
#>  6:  3.19152291 -0.5505672       25 5.819304e-01
#>  7:  3.08769369 -0.7893935       25 4.298820e-01
#>  8: 31.23661380  7.3351834       26 2.214181e-13
#>  9: -0.01010075 -3.0526817       26 2.268064e-03
#> 10:  6.95911596  1.7887180       26 7.366023e-02
#> 11:  8.77732040  2.2313450       26 2.565828e-02
#> 12:  1.80736393 -1.1428504       26 2.531007e-01
#> 13:  2.15247129 -1.1238888       26 2.610602e-01
#> 14: 29.30932132 10.1493773       26 3.334845e-24
#> 15: -0.02790942 -4.2836487       26 1.838531e-05
#> 16:  6.10233579  1.9160734       26 5.535576e-02
#> 17:  8.63642301  2.8413747       26 4.491950e-03
#> 18:  2.36807440 -0.7111255       26 4.770065e-01
#> 19:  5.79655303  0.4924532       26 6.223990e-01
#> 20: 19.19731961  0.7597186       26 4.474228e-01
#> 21:  7.41256416  1.3404389       26 1.801027e-01
#> 22:  9.79620695  3.9818024       26 6.839463e-05
#> 23: 10.56724960  2.6245797       26 8.675598e-03
#> 24:  2.97455903 -0.8961457       26 3.701750e-01
#> 25:  2.87941055 -1.0215543       26 3.069919e-01
#>         CI_high          t df_error            p
ml$forest_data
#>     focal_term    variable         term  term_label   class level level_no  n
#> 1: factor(cyl) factor(cyl) factor(cyl)4 factor(cyl)  factor     4        1 11
#> 2: factor(cyl)        <NA> factor(cyl)6 factor(cyl)  factor     6        2  7
#> 3: factor(cyl)        <NA> factor(cyl)8 factor(cyl)  factor     8        3 14
#> 4:        disp        disp         disp        disp numeric  <NA>       NA 32
#> 5:          hp          hp           hp          hp numeric  <NA>       NA 32
#> 6:        drat        drat         drat        drat numeric  <NA>       NA 32
#>       estimate          SE   CI       CI_low     CI_high         t df_error
#> 1:  0.00000000          NA   NA           NA          NA        NA       NA
#> 2: -5.34044537 1.887666208 0.95  -9.04020315 -1.64068758 -2.829126       25
#> 3: -8.50258933 3.046261922 0.95 -14.47315299 -2.53202568 -2.791155       25
#> 4: -0.02821806 0.009243695 0.95  -0.04633537 -0.01010075 -3.052682       26
#> 5: -0.05145024 0.012010845 0.95  -0.07499106 -0.02790942 -4.283649       26
#> 6:  3.01056864 2.245957350 0.95  -1.39142687  7.41256416  1.340439       26
#>               p reference
#> 1:           NA      TRUE
#> 2: 4.667533e-03     FALSE
#> 3: 5.252031e-03     FALSE
#> 4: 2.268064e-03     FALSE
#> 5: 1.838531e-05     FALSE
#> 6: 1.801027e-01     FALSE
ml$plot_forest()

R6 class representing a list of regression model

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 `build()`

Method `plot_forest()`

Method `print()`

Method `clone()`