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Package {R6Nomogram}

Version: 1.0
Title: Create, Edit, and Plot Nomograms using R6 Objects
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Maintainer: Greg Snow <538280@gmail.com>
Description: Nomograms are a type of plot for displaying linear models. A scale is plotted for each predictor in the model that translates values of the variable into "points", the sum of the "points" is then looked up on another scale to find the final prediction from the model. This package provides an R6 object constructor that does the computations for you to create an object representing the nomogram for the model. Methods and fields in the object allow you to customize the nomogram. You can then plot the nomogram, further customize, replot, etc. These types of nomograms are described in Harrell (2015) <doi:10.1007/978-3-319-19425-7>.
URL: https://github.com/glsnow/R6Nomogram
Imports: R6
Suggests: quarto
Encoding: UTF-8
Config/roxygen2/version: 8.0.0
VignetteBuilder: quarto
NeedsCompilation: no
Packaged: 2026-06-30 19:03:53 UTC; gls33
Author: Greg Snow [aut, cre]
Repository: CRAN
Date/Publication: 2026-07-06 13:50:17 UTC

Nomogram R6 Object

Description

R6Nomogram builds and plots a nomogram from a fitted regression model (e.g. lm, glm, coxph, ...). Given the model and the data used to fit it, the object computes, for each predictor (or interaction term) in the model, an axis that maps predictor values to a common "points" scale. The points scale is in turn related back to the model's linear predictor and to the fitted response, so that the nomogram can be read by summing points across predictors and converting the total back to a predicted response.

Details

The construction of a nomogram proceeds through a small pipeline of steps, all of which are run automatically by initialize() by default (and can also be re-run individually if axes need to be adjusted):

  1. new or initialize - initialize object and store model, terms, and newdat in the object.

  2. PopulateVars() - extract variable names, term-level predictions, and response predictions from the model and data.

  3. create.x() - build sorted, de-duplicated predictor values and the corresponding points for each axis.

  4. shift() - shift each axis so that point values start at a convenient origin (by default, 0).

  5. scale() - rescale all axes so that the maximum number of points is a convenient round number (by default, 100).

  6. pretty() / pretty.y() - choose "pretty" tick locations for the predictor axes and for the total points / linear predictor / response axis.

Once built, plot() draws the nomogram, and tables() returns the same information as a list of data frames (e.g. for use outside of a plot).

Public fields

model

The fitted model object (e.g. from lm, glm, coxph, etc.) used to construct the nomogram.

terms

The terms object describing the model formula (generated by the predict method with type='terms').

newdata

The data frame used to build the nomogram, either supplied by the user or extracted from model.

var.names

Character vector of the variable names appearing in the model formula (excluding the response).

x.names

Character vector of names for each predictor/interaction axis on the nomogram (interaction terms are joined with ":").

x.labels

Character vector of axis labels used when plotting (defaults to x.names).

orig.x

Data frame of the original predictor variable values extracted from newdata.

orig.terms

Data frame of the model's term-level (partial) predictions, one column per term.

orig.response

Vector of unique predicted response values (response scale).

x.vals

List, one element per axis, of the unique, sorted predictor (or interaction) values.

x.points

List, one element per axis, of the points corresponding to x.vals.

x.pretty.vals

List of "pretty" axis tick values used for plotting.

x.pretty.points

List of point positions corresponding to x.pretty.vals.

x.y.offsets

List controlling the vertical offset/justification of axis labels when plotting.

scale.c

Numeric scaling constant relating points back to the linear predictor scale.

constant

The model intercept/offset constant, adjusted as points are shifted and scaled.

total.points

Numeric vector of total points corresponding to the unique fitted values.

linear.predictor

Numeric vector of linear predictor values corresponding to total.points.

pretty.total.points

Numeric vector of "pretty" total-point tick values for the response axis.

pretty.linear.predictor

Numeric vector of linear predictor values matching pretty.total.points.

pretty.response

Numeric vector of response values matching pretty.total.points.

plot.lp

Logical; whether to plot a separate linear-predictor axis in addition to total points and response.

v.pos.x

Numeric vector of vertical plotting positions for the predictor axes.

v.pos.r

Numeric vector of vertical plotting positions for the total points / linear predictor / response axes.

options

List of plotting options: tik.len, txt.pos, points.nint, signif.digits, text.par, line.par, and tick.par. The last 3 default to empty lists, but can be lists with any of the parameters that can be passed to text or segments. They can also be a list of lists with the element names matching self$x.names to give different options for each scale.

points.lab

Label for the "Points" axis.

total.points.lab

Label for the "Total Points" axis.

lp.lab

Label for the "Linear Predictor" axis.

resp.lab

Label for the "Response" axis.

tp.range

Numeric vector giving the range of total points used to position the response axis.

verbose

Logical; whether to print progress messages.

par

list of parameter settings in place during the last plot. This can be used with the par function if you are manually adding to the plot.

Methods

Public methods

R6Nomogram$new()

Construct a new R6Nomogram. Runs the full construction pipeline (loading data, populating variables, creating axes, shifting, scaling, and prettying) up to the requested steps.

Usage
R6Nomogram$new(
  model,
  newdata,
  verbose = TRUE,
  steps = Inf,
  type.terms = "terms",
  type.response = "response"
)
Arguments
model

A fitted model object (e.g. from lm, glm, or coxph).

newdata

Optional data frame to use instead of the data stored in/with model. If missing, the data is recovered from model$data, the data argument of model$call, or model.frame(model), in that order.

verbose

Logical; print progress messages for each step.

steps

Numeric; how many construction steps to run (useful for stopping partway through, e.g. to inspect or adjust axes by hand before continuing). Defaults to Inf, i.e. run all steps.

type.terms

The type argument passed to predict() to obtain term-level predictions.

type.response

The type argument passed to predict() to obtain response-scale predictions.

Returns

The object itself, invisibly.

R6Nomogram$PopulateVars()

Extract variable names, term-level predictions, and response predictions from model and newdata, and use them to populate x.names, x.labels, orig.x, orig.terms, orig.response, constant, total.points, and linear.predictor.

Usage
R6Nomogram$PopulateVars(
  model = self$model,
  newdata = self$newdata,
  type.terms = "terms",
  type.response = "response"
)
Arguments
model

A fitted model object. Defaults to self$model.

newdata

A data frame of predictor values. Defaults to self$newdata.

type.terms

The type argument passed to predict() to obtain term-level predictions.

type.response

The type argument passed to predict() to obtain response-scale predictions.

Returns

The object itself, invisibly.

R6Nomogram$create.x()

Build, for each axis (predictor or interaction term), the unique, sorted predictor values (x.vals) and their corresponding points (x.points), derived from orig.x and orig.terms.

Usage
R6Nomogram$create.x()
Returns

The object itself, invisibly.

R6Nomogram$shift()

Shift the points for one or more axes by a constant offset. By default, each axis named in w is shifted so that its minimum point value becomes 0; total.points and constant are updated to match so that the overall model is unaffected.

Usage
R6Nomogram$shift(w = self$x.names, v, update.constant = TRUE)
Arguments
w

Character vector of axis names (from x.names) to shift. Defaults to all axes.

v

Numeric vector of shift amounts, one per element of w (matched by name if named). If missing, each axis is shifted so that the minimum number of points is 0.

update.constant

Logical; if TRUE (the default), total.points and constant are updated so totals remain consistent with the shifted axes.

Returns

The object itself, invisibly.

R6Nomogram$scale()

Rescale all axes (and total.points) so that the largest point value across all axes equals max.points. scale.c is updated so that points can still be converted back to the linear predictor scale.

Usage
R6Nomogram$scale(max.points = 100)
Arguments
max.points

The desired maximum number of points. Defaults to 100.

Returns

The object itself, invisibly.

R6Nomogram$pretty()

Choose "pretty" tick locations for one or more predictor axes, by interpolating (via a natural spline of points on values) the points corresponding to each chosen tick value. Results are stored in x.pretty.vals and x.pretty.points. Factor axes are left as-is (every level is used as a "pretty" value).

Usage
R6Nomogram$pretty(w = self$x.names, v)
Arguments
w

Character vector of axis names (from x.names) to process. Defaults to all axes.

v

Controls the chosen tick values for each axis in w. If missing, ticks are chosen automatically via axisTicks(). If a single number, it is used as the nint argument to axisTicks(). If a list, each element gives the tick values for the corresponding axis (matched by name). If a numeric vector, it is used directly as the tick values for every axis in w.

Returns

The object itself, invisibly.

R6Nomogram$pretty.y()

Choose "pretty" tick locations for the total points / linear predictor / response axis, by interpolating (via a natural spline of response on total points) the response corresponding to each chosen tick value. Results are stored in pretty.total.points, pretty.linear.predictor, and pretty.response.

Usage
R6Nomogram$pretty.y(v)
Arguments
v

Controls the chosen total-points tick values. If missing, ticks are chosen automatically via axisTicks(). If a single number, it is used as the nint argument to axisTicks(). Otherwise, used directly as the tick values.

Returns

The object itself, invisibly.

R6Nomogram$plot()

Plot the nomogram. Draws the predictor axes (plot.x) and/or the total points / linear predictor / response axes (plot.y) using base graphics, with appearance controlled by self$options (text.par, line.par, tick.par, etc.). If predict is supplied, a prediction line is drawn connecting the predicted points on each predictor axis to the corresponding total on the response axis.

Usage
R6Nomogram$plot(plot.x = TRUE, plot.y = TRUE, predict, ...)
Arguments
plot.x

Logical; draw the predictor axes. Default TRUE.

plot.y

Logical; draw the total points / linear predictor / response axes. Default TRUE.

predict

Optional named list or data frame of predictor values for which to draw a prediction line/segments on the nomogram.

...

Additional graphical parameters, passed to par().

Returns

The object itself, invisibly.

R6Nomogram$grconvertX()

Convert a value from one nomogram axis scale to the points scale. For from = "Total Points", converts a total-points value to its plotting position on the points axis. For any other axis name, converts a predictor value (numeric or factor level) to its points value via spline interpolation (numeric axes) or direct lookup (factor axes).

Usage
R6Nomogram$grconvertX(x, from = "Total Points", verbose)
Arguments
x

Value(s) to convert.

from

Character; the axis to convert from. Either "Total Points" (the default) or one of x.names.

verbose

Logical; print progress messages. Defaults to self$verbose.

Returns

The converted value(s), or NA (with a warning) if from does not match "Total Points" or any axis name.

R6Nomogram$tables()

Summarize the nomogram as a list of data frames: one data frame per predictor axis (giving x.pretty.vals and x.pretty.points), plus a final Response data frame giving pretty.total.points, pretty.linear.predictor, and pretty.response.

Usage
R6Nomogram$tables()
Returns

A named list of data frames.

R6Nomogram$clone()

The objects of this class are cloneable with this method.

Usage
R6Nomogram$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Author(s)

Greg Snow <538280@gmail.com>

References

Regression Modeling Strategies, Frank Harrell <doi:10.1007/978-3-319-19425-7>

See Also

The nomogram function in the rms package

Examples

mtcars2 <- mtcars
mtcars2$cyl <- factor(mtcars2$cyl)
mtcars2$gear <- factor(mtcars2$gear)
mtcars2$vs <- factor(mtcars2$vs)

fit <- glm(mpg ~ poly(wt,2) + poly(disp,2) + cyl*gear + vs,
  data=mtcars2, family=gaussian(link=inverse))
  
n1 <- R6Nomogram$new(fit)
n1$plot()

n1$options$tik.len <- 0.4
n1$options$txt.pos <- 1.2
n1$options$signif.digits <- 3
n1$x.pretty.vals$`cyl:gear` <-
  c("Other", "", "", "", "6:4", "", "6:5", "8:5")
n1$pretty.y(seq(60, 200, by=10))
n1$pretty('wt', c(1.5, 2, 3, 3.5, 4, 4.25, 4.5, 
                  4.75, 5, 5.25, 5.4))
n1$pretty('disp', c(75, 100, 125, 150, 175, 200, 250,
                    300, 400, 450))
n1$plot.lp <- TRUE
n1$v.pos.r <- NULL # it will be recreated properly in next plot
n1$lp.lab <- "G/M"
n1$resp.lab <- "M/G"
n1$options$text.par[['linear predictor']] <- list(cex=0.8)

n1$plot()

rm(mtcars2)

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