summaryS {Hmisc} | R Documentation

## Summarize Multiple Response Variables and Make Multipanel Scatter or Dot Plot

### Description

Multiple left-hand formula variables along with right-hand side conditioning variables are reshaped into a "tall and thin" data frame if `fun` is not specified. The resulting raw data can be plotted with the `plot` method using user-specified `panel` functions for `lattice` graphics, typically to make a scatterplot or `loess` smooths, or both. The `Hmisc` `panel.plsmo` function is handy in this context. Instead, if `fun` is specified, this function takes individual response variables (which may be matrices, as in `Surv` objects) and creates one or more summary statistics that will be computed while the resulting data frame is being collapsed to one row per condition. The `plot` method in this case plots a multi-panel dot chart using the `lattice` `dotplot` function if `panel` is not specified to `plot`. There is an option to print selected statistics as text on the panels. `summaryS` pays special attention to `Hmisc` variable annotations: `label, units`. When `panel` is specified in addition to `fun`, a special `x-y` plot is made that assumes that the `x`-axis variable (typically time) is discrete. This is used for example to plot multiple quantile intervals as vertical lines next to the main point. A special panel function `mvarclPanel` is provided for this purpose.

When `fun` is given and `panel` is omitted, and the result of `fun` is a vector of more than one statistic, the first statistic is taken as the main one. Any columns with names not in `textonly` will figure into the calculation of axis limits. Those in `textonly` will be printed right under the dot lines in the dot chart. Statistics with names in `textplot` will figure into limits, be plotted, and printed. `pch.stats` can be used to specify symbols for statistics after the first column. When `fun` computed three columns that are plotted, columns two and three are taken as confidence limits for which horizontal "error bars" are drawn. Two levels with different thicknesses are drawn if there are four plotted summary statistics beyond the first.

`mbarclPanel` is used to draw multiple vertical lines around the main points, such as a series of quantile intervals stratified by `x` and paneling variables. If `mbarclPanel` finds a column of an arument `yother` that is named `"se"`, and if there are exactly two levels to a superpositioning variable, the half-height of the approximate 0.95 confidence interval for the difference between two point estimates is shown, positioned at the midpoint of the two point estimates at an `x` value. This assume normality of point estimates, and the standard error of the difference is the square root of the sum of squares of the two standard errors. By positioning the intervals in this fashion, a failure of the two point estimates to touch the half-confidence interval is consistent with rejecting the null hypothesis of no difference at the 0.05 level.

`medvPanel` takes raw data and plots median `y` vs. `x`, along with confidence intervals and half-interval for the difference in medians as with `mbarclPanel`. Quantile intervals are optional. Very transparent vertical violin plots are added by default. Unlike `panel.violin`, only half of the violin is plotted, and when there are two superpose groups they are side-by-side in different colors.

### Usage

    summaryS(formula, fun = NULL, data = NULL, subset = NULL,
             na.action = na.retain, continuous=10, ...)

    ## S3 method for class 'summaryS'
    plot(x, formula=NULL, groups=NULL, panel=NULL,
               paneldoesgroups=FALSE, datadensity=NULL, ylab='',
               funlabel=NULL, textonly='n', textplot=NULL,
               digits=3, custom=NULL,
               xlim=NULL, ylim=NULL, cex.strip=1, cex.values=0.5, pch.stats=NULL,
               key=list(columns=length(groupslevels),
                 x=.75, y=-.04, cex=.9,
                 col=trellis.par.get('superpose.symbol')$col, corner=c(0,1)),
               outerlabels=TRUE, autoarrange=TRUE, scat1d.opts=NULL, ...)

    mbarclPanel(x, y, subscripts, groups=NULL, yother, ...)

    medvPanel(x, y, subscripts, groups=NULL, violin=TRUE, quantiles=FALSE, ...)

### Arguments

`formula` | a formula with possibly multiple left and right-side variables separated by `+`. Analysis (response) variables are on the left and are typically numeric. For `plot`, `formula` is optional and overrides the default formula inferred for the reshaped data frame.
`fun` | an optional summarization function, e.g., `smean.sd`
`data` | optional input data frame
`subset` | optional subsetting criteria
`na.action` | function for dealing with `NA`s when constructing the model data frame
`continuous` | minimum number of unique values for a numeric variable to have to be considered continuous
`...` | ignored for `summaryS` and `mbarclPanel`, passed to `strip` and `panel` for `plot`. Passed to the `density` function by `medvPanel`.
`x` | an object created by `summaryS`. For `mbarclPanel` is an `x`-axis argument provided by `lattice`
`groups` | a character string specifying that one of the conditioning variables is used for superpositioning and not paneling
`panel` | optional `lattice` `panel` function
`paneldoesgroups` | set to `TRUE` if, like `panel.plsmo`, the paneling function internally handles superpositioning for `groups`
`datadensity` | set to `TRUE` to add rug plots etc. using `scat1d`
`ylab` | optional `y`-axis label
`funlabel` | optional axis label for when `fun` is given
`textonly` | names of statistics to print and not plot. By default, any statistic named `"n"` is only printed.
`textplot` | names of statistics to print and plot
`digits` | used if any statistics are printed as text, to specify the number of digits to the right of the decimal point to print
`custom` | a function that customizes formatting of statistics that are printed as text. This is useful for generating plotmath notation. See the example in the tests directory.
`xlim` | optional `x`-axis limits
`ylim` | optional `y`-axis limits
`cex.strip` | size of strip labels
`cex.values` | size of statistics printed as text
`pch.stats` | symbols to use for statistics (not included the one one in columne one) that are plotted. This is a named vectors, with names exactly matching those created by `fun`. When a column does not have an entry in `pch.stats`, no point is drawn for that column.
`key` | `lattice` `key` specification
`outerlabels` | set to `FALSE` to not pass two-way charts through `useOuterStrips`
`autoarrange` | set to `FALSE` to prevent `plot` from trying to optimize which conditioning variable is vertical
`scat1d.opts` | a list of options to specify to `scat1d`
`y, subscripts` | provided by `lattice`
`yother` | passed to the panel function from the `plot` method based on multiple statistics computed
`violin` | controls whether violin plots are included
`quantiles` | controls whether quantile intervals are included

### Value

a data frame with added attributes for `summaryS` or a `lattice` object ready to render for `plot`

### Author(s)

Frank Harrell

### See Also

`summary`, `summarize`

### Examples

    # See tests directory file summaryS.r for more examples
    n <- 100
    set.seed(1)
    d <- data.frame(sbp=rnorm(n, 120, 10),
                    dbp=rnorm(n, 80, 10),
                    age=rnorm(n, 50, 10),
                    days=sample(1:n, n, TRUE),
                    S1=Surv(2*runif(n)), S2=Surv(runif(n)),
                    race=sample(c('Asian', 'Black/AA', 'White'), n, TRUE),
                    sex=sample(c('Female', 'Male'), n, TRUE),
                    treat=sample(c('A', 'B'), n, TRUE),
                    region=sample(c('North America','Europe'), n, TRUE),
                    meda=sample(0:1, n, TRUE), medb=sample(0:1, n, TRUE))

    d <- upData(d, labels=c(sbp='Systolic BP', dbp='Diastolic BP',
                race='Race', sex='Sex', treat='Treatment',
                days='Time Since Randomization',
                S1='Hospitalization', S2='Re-Operation',
                meda='Medication A', medb='Medication B'),
                units=c(sbp='mmHg', dbp='mmHg', age='Year', days='Days'))

    s <- summaryS(age + sbp + dbp ~ days + region + treat,  data=d)
    # plot(s)   # 3 pages
    plot(s, groups='treat', datadensity=TRUE,
         scat1d.opts=list(lwd=.5, nhistSpike=0))
    plot(s, groups='treat', panel=panel.loess, key=list(space='bottom', columns=2),
         datadensity=TRUE, scat1d.opts=list(lwd=.5))

    # Make your own plot using data frame created by summaryP
    # xyplot(y ~ days | yvar * region, groups=treat, data=s,
    #        scales=list(y='free', rot=0))

    # Use loess to estimate the probability of two different types of events as
    # a function of time
    s <- summaryS(meda + medb ~ days + treat + region, data=d)
    pan <- function(...)
       panel.plsmo(..., type='l', label.curves=max(which.packet()) == 1,
                   datadensity=TRUE)
    plot(s, groups='treat', panel=pan, paneldoesgroups=TRUE,
         scat1d.opts=list(lwd=.7), cex.strip=.8)

    # Repeat using intervals instead of nonparametric smoother
    pan <- function(...)  # really need mobs > 96 to est. proportion
      panel.plsmo(..., type='l', label.curves=max(which.packet()) == 1,
                  method='intervals', mobs=5)

    plot(s, groups='treat', panel=pan, paneldoesgroups=TRUE, xlim=c(0, 150))

    # Demonstrate dot charts of summary statistics
    s <- summaryS(age + sbp + dbp ~ region + treat, data=d, fun=mean)
    plot(s)
    plot(s, groups='treat', funlabel=expression(bar(X)))
    # Compute parametric confidence limits for mean, and include sample
    # sizes by naming a column "n"

    f <- function(x) {
      x <- x[! is.na(x)]
      c(smean.cl.normal(x, na.rm=FALSE), n=length(x))
    }
    s <- summaryS(age + sbp + dbp ~ region + treat, data=d, fun=f)
    plot(s, funlabel=expression(bar(X) %+-% t[0.975] %*% s))
    plot(s, groups='treat', cex.values=.65,
         key=list(space='bottom', columns=2,
           text=c('Treatment A:','Treatment B:')))

    # For discrete time, plot Harrell-Davis quantiles of y variables across
    # time using different line characteristics to distinguish quantiles
    d <- upData(d, days=round(days / 30) * 30)
    g <- function(y) {
      probs <- c(0.05, 0.125, 0.25, 0.375)
      probs <- sort(c(probs, 1 - probs))
      y <- y[! is.na(y)]
      w <- hdquantile(y, probs)
      m <- hdquantile(y, 0.5, se=TRUE)
      se <- as.numeric(attr(m, 'se'))
      c(Median=as.numeric(m), w, se=se, n=length(y))
    }
    s <- summaryS(sbp + dbp ~ days + region, fun=g, data=d)
    plot(s, panel=mbarclPanel)
    plot(s, groups='region', panel=mbarclPanel, paneldoesgroups=TRUE)

    # For discrete time, plot median y vs x along with CL for difference,
    # using Harrell-Davis median estimator and its s.e., and use violin
    # plots

    s <- summaryS(sbp + dbp ~ days + region, data=d)
    plot(s, groups='region', panel=medvPanel, paneldoesgroups=TRUE)

    # Proportions and Wilson confidence limits, plus approx. Gaussian
    # based half/width confidence limits for difference in probabilities
    g <- function(y) {
      y <- y[!is.na(y)]
      n <- length(y)
      p <- mean(y)
      se <- sqrt(p * (1. - p) / n)
      structure(c(binconf(sum(y), n), se=se, n=n),
                names=c('Proportion', 'Lower', 'Upper', 'se', 'n'))
    }
    s <- summaryS(meda + medb ~ days + region, fun=g, data=d)
    plot(s, groups='region', panel=mbarclPanel, paneldoesgroups=TRUE)

[Package Hmisc version 4.0-3 Index]