radarplot: A multidimensional 'Table 1 at-a-glance'

In VizOR: Graphical Visualization Tools for Complex Observational Data with Focus on Health Sciences

Description

Plots a specialized radar (aka, ‘star’) plot to compare summary statistics on a handful of covariates, across a multi-dimensional set of comparison groups, as in the standard ‘Table 1’ of experimental studies, where the groups correspond to treatment assignments.

Usage

radarplot(x, data, datadist = getOption("datadist"), xlim = c(-1.4, 1.4),
  ylim = c(-1.2, 1.2), rescale = c("IQR", "range"), treatment = NULL,
  stratify = NULL, strength = NULL, include.na = FALSE, overall = FALSE,
  ...)

Arguments

x	Typically, a formula of the form S~x, S~x|t or S~x|t*u, where S is the (suitably normalized) summary matrix of a summary.formula.cross object having factors {x[, t[, u]]} as covariates. In the resulting radar plot, the x factor will correspond to colored polygons overlaid within each panel of a trellis of 0, 1 or 2 dimensions, defined by the (optional) terms t and u. Alternatively, x may be a fitted model in which the LHS is the experimental outcome, and the RHS includes the spoke variables.
data	Either a summary.formula.cross object (when x is a formula), or a data.frame (when x is a fitted model).
datadist	An rms:datadist object describing the underlying dataset. By suitably modifying datadist$limits, greater control may be achieved over the automatic calculation of strength-of-association measures.
xlim	A positioning parameter that really should be automated!
ylim	A positioning parameter that really should be automated!
rescale	How to rescale continuous covariates for plotting on the spokes
treatment	When x is an outcomes model, identifying the treatment variable (and any variables to stratify on, see below) permits the formula embedded in the model to be used to generate a summary.formula.cross object
stratify	When x is an outcomes model, identifying the treatment variable (see above) and any variables to stratify on, permits the formula embedded in the model to be used to generate a summary.formula.cross object
strength	When the default choice of strength-of-association measure is not suitable, it may be specified explicitly as a named vector with elements corresponding to the spoke variables, in the order they appear in the model formula. The names will be printed at the outer endpoints of the spokes, and will typically be numbers (e.g., odds ratios) rounded to 2-3 significant figures.
include.na	Should the summary include NA values? Passed to the summary.formula call that generates the radarplot data.
overall	Should the summary include an 'ALL' row? Passed to the summary.formula call that generates the radarplot data.
...	Other parameters to be passed to panel.radarplot

Details

The covariates appear on the radii, or 'spokes' of the radarplot, while the several (up to 3) dimensions defining the comparison groups appear as a color-coded overlay, plus up to two trellis dimensions. In generic applications, the client code employs Hmisc::summary.formula(method='cross') to create the multi-dimensional array for plotting, and then rescales the array elements by a linear or affine transformation to the interval [0,1]. Ordered factors may be treated as quasi-continuous variables according to their internal integer representation; logical covariates (and also, conveniently, 2-valued factors) appear as proportions; and unordered categorical variables with n levels may be transformed to n-1 dummy variables, which may then be plotted as proportions. An optional strength parameter may be specified, providing a strength-of-association measure connecting the spoke variables to the experimental outcome. This is reflected graphically by fanning the spokes out into sectors subtending angles proportional to the strength of associations. Used in this way, the radar plot draws attention to those spoke variables with greatest potential to confound the outcome, and so becomes a useful exploratory tool. To provide convenient support for this specialized usage, radarplot optionally accepts a regression model in which the outcome of interest is the regressand, and the spoke variables are regressors. From this model, strength-of-association measures are automatically derived, appropriately to the class of the regression model. Invoked in this way, radarplot is also able to free client code from the burden of generating and rescaling a summary.formula.cross object. At this time, models of class lrm and cph are supported. See the example code below.

Author(s)

David C. Norris

Examples

library(rms)
df <- upData(mtcars,
             #cyl=factor(cyl,levels=2*(2:4),labels=paste(2*(2:4),"cyl", sep="-")),
             #am=factor(am,levels=0:1,labels=c("automatic","manual")),
             #gear=factor(gear,levels=3:5,labels=paste(3:5,"speed", sep="-")),
             labels=c(
               mpg="Miles per gallon"
               ,cyl="Number of cylinders"
               ,disp="Displacement"
               ,hp="Gross horsepower"
               ,drat="Rear axle ratio"
               ,wt="Weight"
               ,qsec="1/4 mile time"
               ,am="Transmission type"
               ,gear="Number of forward gears"
               ,carb="Number of carburetors"
               ),
             units=c(
               wt="lb/1000"
               ,disp="in^3"
               ,qsec="sec"
               ),
             drop='vs'
             )
df$cyl <- factor(df$cyl,levels=2*(2:4),labels=paste(2*(2:4),"cyl", sep="-"))
df$am  <- factor(df$am,levels=0:1,labels=c("automatic","manual"))
df$gear<- factor(df$gear,levels=3:5,labels=paste(3:5,"speed", sep="-"))
s <- summary(cbind(mpg, disp, hp, drat, wt) ~ cyl + gear + am,
             method='cross', overall=TRUE, data=df)
dd <- datadist(df)
radarplot(S ~ cyl | gear*am, data=s, datadist=dd, rescale="range")

## TODO: Provide example of convenient usage with 'lrm' and 'cph' models.

VizOR documentation built on May 30, 2017, 5:29 a.m.