R: Graphical Summarization of Continuous Variables Against a...

summaryRc {Hmisc} | R Documentation

## Graphical Summarization of Continuous Variables Against a Response

### Description

`summaryRc` is a continuous version of `summary.formula` with `method='response'`. It uses the `plsmo` function to compute the possibly stratified `lowess` nonparametric regression estimates, and plots them along with the data density, with selected quantiles of the overall distribution (over strata) of each `x` shown as arrows on top of the graph. All the `x` variables must be numeric and continuous or nearly continuous.

### Usage

    summaryRc(formula, data=NULL, subset=NULL,
              na.action=NULL, fun = function(x) x,
              na.rm = TRUE, ylab=NULL, ylim=NULL, xlim=NULL,
              nloc=NULL, datadensity=NULL,
              quant = c(0.05, 0.1, 0.25, 0.5, 0.75,
                        0.90, 0.95), quantloc=c('top','bottom'),
              cex.quant=.6, srt.quant=0,
              bpplot = c('none', 'top', 'top outside', 'top inside', 'bottom'),
              height.bpplot=0.08,
              trim=NULL, test = FALSE, vnames = c('labels', 'names'), ...)

### Arguments

`formula` | An R formula with additive effects. The `formula` may contain one or more invocations of the `stratify` function whose arguments are defined below. This causes the entire analysis to be stratified by cross-classifications of the combined list of stratification factors. This stratification will be reflected as separate `lowess` curves.
`data` | name or number of a data frame. Default is the current frame.
`subset` | a logical vector or integer vector of subscripts used to specify the subset of data to use in the analysis. The default is to use all observations in the data frame.
`na.action` | function for handling missing data in the input data. The default is a function defined here called `na.retain`, which keeps all observations for processing, with missing variables or not.
`fun` | function for transforming `lowess` estimates. Default is the identity function.
`na.rm` | `TRUE` (the default) to exclude `NA`s before passing data to `fun` to compute statistics, `FALSE` otherwise.
`ylab` | `y`-axis label. Default is label attribute of `y` variable, or its name.
`ylim` | `y`-axis limits. By default each graph is scaled on its own.
`xlim` | a list with elements named as the variable names appearing on the `x`-axis, with each element being a 2-vector specifying lower and upper limits. Any variable not appearing in the list will have its limits computed and possibly `trim`med.
`nloc` | location for sample size. Specify `nloc=FALSE` to suppress, or `nloc=list(x=,y=)` where `x,y` are relative coordinates in the data window. Default position is in the largest empty space.
`datadensity` | see `plsmo`. Defaults to `TRUE` if there is a `stratify` variable, `FALSE` otherwise.
`quant` | vector of quantiles to use for summarizing the marginal distribution of each `x`. This must be numbers between 0 and 1 inclusive. Use `NULL` to omit quantiles.
`quantloc` | specify `quantloc='bottom'` to place at the bottom of each plot rather than the default
`cex.quant` | character size for writing which quantiles are represented. Set to `0` to suppress quantile labels.
`srt.quant` | angle for text for quantile labels
`bpplot` | if not `'none'` will draw extended box plot at location given by `bpplot`, and quantiles discussed above will be suppressed. Specifying `bpplot='top'` is the same as specifying `bpplot='top inside'`.
`height.bpplot` | height in inches of the horizontal extended box plot
`trim` | The default is to plot from the 10th smallest to the 10th largest `x` if the number of non-NAs exceeds 200, otherwise to use the entire range of `x`. Specify another quantile to use other limits, e.g., `trim=0.01` will use the first and last percentiles
`test` | Set to `TRUE` to plot test statistics (not yet implemented).
`vnames` | By default, plots are usually labeled with variable labels (see the `label` and `sas.get` functions). To use the shorter variable names, specify `vnames="names"`.
`...` | arguments passed to `plsmo`

### Value

no value is returned

### Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University
f.harrell@vanderbilt.edu

### See Also

`plsmo`, `stratify`, `label`, `formula`, `panel.bpplot`

### Examples

    options(digits=3)
    set.seed(177)
    sex <- factor(sample(c("m","f"), 500, rep=TRUE))
    age <- rnorm(500, 50, 5)
    bp  <- rnorm(500, 120, 7)
    units(age) <- 'Years'; units(bp) <- 'mmHg'
    label(bp) <- 'Systolic Blood Pressure'
    L <- .5*(sex == 'm') + 0.1 * (age - 50)
    y <- rbinom(500, 1, plogis(L))
    par(mfrow=c(1,2))
    summaryRc(y ~ age + bp)
    # For x limits use 1st and 99th percentiles to frame extended box plots
    summaryRc(y ~ age + bp, bpplot='top', datadensity=FALSE, trim=.01)
    summaryRc(y ~ age + bp + stratify(sex),
              label.curves=list(keys='lines'), nloc=list(x=.1, y=.05))
    y2 <- rbinom(500, 1, plogis(L + .5))
    Y <- cbind(y, y2)
    summaryRc(Y ~ age + bp + stratify(sex),
              label.curves=list(keys='lines'), nloc=list(x=.1, y=.05))

[Package Hmisc version 4.0-3 Index]