# summaryRc

##### Graphical Summarization of Continuous Variables Against a Response

`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.

- Keywords
- hplot

##### 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

##### See Also

##### Examples

```
# NOT RUN {
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))
# }
```

*Documentation reproduced from package Hmisc, version 4.3-1, License: GPL (>= 2)*