R: Summarize Mixed Data Types vs. Groups

summaryM {Hmisc} | R Documentation

## Summarize Mixed Data Types vs. Groups

### Description

`summaryM` summarizes the variables listed in an S formula, computing descriptive statistics and optionally statistical tests for group differences. This function is typically used when there are multiple left-hand-side variables that are independently against by groups marked by a single right-hand-side variable. The summary statistics may be passed to `print` methods, `plot` methods for making annotated dot charts and extended box plots, and `latex` methods for typesetting tables using LaTeX. The `html` method uses `htmlTable::htmlTable` to typeset the table in html, by passing information to the `latex` method with `html=TRUE`. This is for use with RMarkdown under RStudio. The `print` methods use the `print.char.matrix` function to print boxed tables.

The `plot` method creates `plotly` graphics if `options(grType='plotly')`, otherwise base graphics are used. `plotly` graphics provide extra information such as which quantile is being displayed when hovering the mouse. Test statistics are displayed by hovering over the mean.

Continuous variables are described by three quantiles (quartiles by default) when printing, or by the following quantiles when plotting expended box plots using the `bpplt` function: 0.05, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 0.95. The box plots are scaled to the 0.025 and 0.975 quantiles of each continuous left-hand-side variable. Categorical variables are described by counts and percentages.

The left hand side of `formula` may contain `mChoice` ("multiple choice") variables. When `test=TRUE` each choice is tested separately as a binary categorical response.

The `plot` method for `method="reverse"` creates a temporary function `Key` as is done by the `xYplot` and `Ecdf.formula` functions. After `plot` runs, you can type `Key()` to put a legend in a default location, or e.g. `Key(locator(1))` to draw a legend where you click the left mouse button. This key is for categorical variables, so to have the opportunity to put the key on the graph you will probably want to use the command `plot(object, which="categorical")`. A second function `Key2` is created if continuous variables are being plotted. It is used the same as `Key`. If the `which` argument is not specified to `plot`, two pages of plots will be produced. If you don't define `par(mfrow=)` yourself, `plot.summaryM` will try to lay out a multi-panel graph to best fit all the individual charts for continuous variables.

### Usage

    summaryM(formula, groups=NULL, data=NULL, subset, na.action=na.retain,
             overall=FALSE, continuous=10, na.include=FALSE,
             quant=c(0.025, 0.05, 0.125, 0.25, 0.375, 0.5, 0.625,
                     0.75, 0.875, 0.95, 0.975),
             nmin=100, test=FALSE,
             conTest=conTestkw, catTest=catTestchisq,
             ordTest=ordTestpo)

    ## S3 method for class 'summaryM'
    print(x, digits, prn = any(n != N),
          what=c('proportion', '%'), pctdig = if(what == '%') 0 else 2,
          npct = c('numerator', 'both', 'denominator', 'none'),
          exclude1 = TRUE, vnames = c('labels', 'names'), prUnits = TRUE,
          sep = '/', abbreviate.dimnames = FALSE,
          prefix.width = max(nchar(lab)), min.colwidth, formatArgs=NULL, round=NULL,
          prtest = c('P','stat','df','name'), prmsd = FALSE, long = FALSE,
          pdig = 3, eps = 0.001, prob = c(0.25, 0.5, 0.75), prN = FALSE, ...)

    ## S3 method for class 'summaryM'
    plot(x, vnames = c('labels', 'names'),
         which = c('both', 'categorical', 'continuous'), vars=NULL,
         xlim = c(0,1),
         xlab = 'Proportion',
         pch = c(16, 1, 2, 17, 15, 3, 4, 5, 0), exclude1 = TRUE,
         main, ncols=2,
         prtest = c('P', 'stat', 'df', 'name'), pdig = 3, eps = 0.001,
         conType = c('bp', 'dot', 'raw'), cex.means = 0.5, cex=par('cex'),
         height='auto', width=700, ...)

    ## S3 method for class 'summaryM'
    latex(object, title =
          first.word(deparse(substitute(object))),
          file=paste(title, 'tex', sep='.'), append=FALSE, digits,
          prn = any(n != N), what=c('proportion', '%'),
          pctdig = if(what == '%') 0 else 2,
          npct = c('numerator', 'both', 'denominator', 'slash', 'none'),
          npct.size = if(html) mspecs$html$smaller else 'scriptsize',
          Nsize = if(html) mspecs$html$smaller else 'scriptsize',
          exclude1 = TRUE,
          vnames=c("labels", "names"), prUnits = TRUE, middle.bold = FALSE,
          outer.size = if(html) mspecs$html$smaller else "scriptsize",
          caption, rowlabel = "", rowsep=html,
          insert.bottom = TRUE, dcolumn = FALSE, formatArgs=NULL, round=NULL,
          prtest = c('P', 'stat', 'df', 'name'), prmsd = FALSE,
          msdsize = if(html) function(x) x else NULL, brmsd=FALSE,
          long = FALSE, pdig = 3, eps = 0.001,
          auxCol = NULL, table.env=TRUE, tabenv1=FALSE, prob=c(0.25, 0.5, 0.75),
          prN=FALSE, legend.bottom=FALSE, html=FALSE,
          mspecs=markupSpecs, ...)

    ## S3 method for class 'summaryM'
    html(object, ...)

### Arguments

`formula` | An S formula with additive effects. There may be several variables on the right hand side separated by "+", or the numeral `1`, indicating that there is no grouping variable so that only margin summaries are produced. The right hand side variable, if present, must be a discrete variable producing a limited number of groups. On the left hand side there may be any number of variables, separated by "+", and these may be of mixed types. These variables are analyzed separately by the grouping variable.
`groups` | if there is more than one right-hand variable, specify `groups` as a character string containing the name of the variable used to produce columns of the table. The remaining right hand variables are combined to produce levels that cause separate tables or plots to be produced.
`x` | an object created by `summaryM`. For `conTestkw` a numeric vector, and for `ordTestpo`, a numeric or factor variable that can be considered ordered
`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.
`overall` | Setting `overall=TRUE` makes a new column with overall statistics for the whole sample. If `test=TRUE` these marginal statistics are ignored in doing statistical tests.
`continuous` | specifies the threshold for when a variable is considered to be continuous (when there are at least `continuous` unique values). `factor` variables are always considered to be categorical no matter how many levels they have.
`na.include` | Set `na.include=TRUE` to keep missing values of categorical variables from being excluded from the table.
`nmin` | For categories of the response variable in which there are less than or equal to `nmin` non-missing observations, the raw data are retained for later plotting in place of box plots.
`test` | Set to `TRUE` to compute test statistics using tests specified in `conTest` and `catTest`.
`conTest` | a function of two arguments (grouping variable and a continuous variable) that returns a list with components `P` (the computed P-value), `stat` (the test statistic, either chi-square or F), `df` (degrees of freedom), `testname` (test name), `namefun` (`"chisq", "fstat"`), `statname` (statistic name), an optional component `latexstat` (LaTeX representation of `statname`), an optional component `plotmathstat` (for R - the `plotmath` representation of `statname`, as a character string), and an optional component `note` that contains a character string note about the test (e.g., `"test not done because n < 5"`). `conTest` is applied to continuous variables on the right-hand-side of the formula when `method="reverse"`. The default uses the `spearman2` function to run the Wilcoxon or Kruskal-Wallis test using the F distribution.
`catTest` | a function of a frequency table (an integer matrix) that returns a list with the same components as created by `conTest`. By default, the Pearson chi-square test is done, without continuity correction (the continuity correction would make the test conservative like the Fisher exact test).
`ordTest` | a function of a frequency table (an integer matrix) that returns a list with the same components as created by `conTest`. By default, the Proportional odds likelihood ratio test is done.
`...` | For `Key` and `Key2` these arguments are passed to `key`, `text`, or `mtitle`. For `print` methods these are optional arguments to `print.char.matrix`. For `latex` methods these are passed to `latex.default`. For `html` the arguments are passed the `latex.summaryM`, and the arguments may not include `file`.
`object` | an object created by `summaryM`
`quant` | vector of quantiles to use for summarizing continuous variables. These must be numbers between 0 and 1 inclusive and must include the numbers 0.5, 0.25, and 0.75 which are used for printing and for plotting quantile intervals. The outer quantiles are used for scaling the x-axes for such plots. Specify outer quantiles as `0` and `1` to scale the x-axes using the whole observed data ranges instead of the default (a 0.95 quantile interval). Box-percentile plots are drawn using all but the outer quantiles.
`prob` | vector of quantiles to use for summarizing continuous variables. These must be numbers between 0 and 1 inclusive and have previously been included in the `quant` argument of `summaryM`. The vector must be of length three. By default it contains 0.25, 0.5, and 0.75. Warning: specifying 0 and 1 as two of the quantiles will result in computing the minimum and maximum of the variable. As for many random variables the minimum will continue to become smaller as the sample size grows, and the maximum will continue to get larger. Thus the min and max are not recommended as summary statistics.
`vnames` | By default, tables and plots are usually labeled with variable labels (see the `label` and `sas.get` functions). To use the shorter variable names, specify `vnames="name"`.
`pch` | vector of plotting characters to represent different groups, in order of group levels.
`abbreviate.dimnames` | see `print.char.matrix`
`prefix.width` | see `print.char.matrix`
`min.colwidth` | minimum column width to use for boxes printed with `print.char.matrix`. The default is the maximum of the minimum column label length and the minimum length of entries in the data cells.
`formatArgs` | a list containing other arguments to pass to `format.default` such as `scientific`, e.g., `formatArgs=list(scientific=c(-5,5))`. For `print.summary.formula.reverse` and `format.summary.formula.reverse`, `formatArgs` applies only to statistics computed on continuous variables, not to percents, numerators, and denominators. The `round` argument may be preferred.
`digits` | number of significant digits to print. Default is to use the current value of the `digits` system option.
`what` | specifies whether proportions or percentages are to be printed or LaTeX'd
`pctdig` | number of digits to the right of the decimal place for printing percentages or proportions. The default is zero if `what='%'`, so percents will be rounded to the nearest percent. The default is 2 for proportions.
`prn` | set to `TRUE` to print the number of non-missing observations on the current (row) variable. The default is to print these only if any of the counts of non-missing values differs from the total number of non-missing values of the left-hand-side variable.
`prN` | set to `TRUE` to print the number of non-missing observations on rows that contain continuous variables.
`npct` | specifies which counts are to be printed to the right of percentages. The default is to print the frequency (numerator of the percent) in parentheses. You can specify `"both"` to print both numerator and denominator as a fraction, `"denominator"`, `"slash"` to typeset horizontally using a forward slash, or `"none"`.
`npct.size` | the size for typesetting `npct` information which appears after percents. The default is `"scriptsize"`.
`Nsize` | When a second row of column headings is added showing sample sizes, `Nsize` specifies the LaTeX size for these subheadings. Default is `"scriptsize"`.
`exclude1` | By default, `summaryM` objects will be printed, plotted, or typeset by removing redundant entries from percentage tables for categorical variables. For example, if you print the percent of females, you don't need to print the percent of males. To override this, set `exclude1=FALSE`.
`prUnits` | set to `FALSE` to suppress printing or latexing `units` attributes of variables, when `method='reverse'` or `'response'`
`sep` | character to use to separate quantiles when printing tables
`prtest` | a vector of test statistic components to print if `test=TRUE` was in effect when `summaryM` was called. Defaults to printing all components. Specify `prtest=FALSE` or `prtest="none"` to not print any tests. This applies to `print`, `latex`, and `plot` methods.
`round` | Specify `round` to round the quantiles and optional mean and standard deviation to `round` digits after the decimal point. Set `round='auto'` to try an automatic choice.
`prmsd` | set to `TRUE` to print mean and SD after the three quantiles, for continuous variables
`msdsize` | defaults to `NULL` to use the current font size for the mean and standard deviation if `prmsd` is `TRUE`. Set to a character string or function to specify an alternate LaTeX font size.
`brmsd` | set to `TRUE` to put the mean and standard deviation on a separate line, for html
`long` | set to `TRUE` to print the results for the first category on its own line, not on the same line with the variable label
`pdig` | number of digits to the right of the decimal place for printing P-values. Default is `3`. This is passed to `format.pval`.
`eps` | P-values less than `eps` will be printed as `< eps`. See `format.pval`.
`auxCol` | an optional auxiliary column of information, right justified, to add in front of statistics typeset by `latex.summaryM`. This argument is a list with a single element that has a name specifying the column heading. If this name includes a newline character, the portions of the string before and after the newline form respectively the main heading and the subheading (typically set in smaller font), respectively. See the `extracolheads` argument to `latex.default`. `auxCol` is filled with blanks when a variable being summarized takes up more than one row in the output. This happens with categorical variables.
`table.env` | set to `FALSE` to use `tabular` environment with no caption
`tabenv1` | set to `TRUE` in the case of stratification when you want only the first stratum's table to be in a table environment. This is useful when using `hyperref`.
`which` | Specifies whether to plot results for categorical variables, continuous variables, or both (the default).
`vars` | Subscripts (indexes) of variables to plot for `plotly` graphics. Default is to plot all variables of each type (categorical or continuous).
`conType` | For drawing plots for continuous variables, extended box plots (box-percentile-type plots) are drawn by default, using all quantiles in `quant` except for the outermost ones which are using for scaling the overall plot based on the non-stratified marginal distribution of the current response variable. Specify `conType='dot'` to draw dot plots showing the three quartiles instead. For extended box plots, means are drawn with a solid dot and vertical reference lines are placed at the three quartiles. Specify `conType='raw'` to make a strip chart showing the raw data. This can only be used if the sample size for each right-hand-side group is less than or equal to `nmin`.
`cex.means` | character size for means in box-percentile plots; default is .5
`cex` | character size for other plotted items
`height,width` | dimensions in pixels for the `plotly` `subplot` object containing all the extended box plots. If `height="auto"`, `plot.summaryM` will set `height` based on the number of continuous variables and `ncols` or for dot charts it will use `Hmisc::plotlyHeightDotchart`. At present `height` is ignored for extended box plots due to vertical spacing problem with `plotly` graphics.
`xlim` | vector of length two specifying x-axis limits. This is only used for plotting categorical variables. Limits for continuous variables are determined by the outer quantiles specified in `quant`.
`xlab` | x-axis label
`main` | a main title. This applies only to the plot for categorical variables.
`ncols` | number of columns for `plotly` graphics for extended box plots. Defaults to 2. Recommendation is for 1-2.
`caption` | character string containing LaTeX table captions.
`title` | name of resulting LaTeX file omitting the `.tex` suffix. Default is the name of the `summary` object. If `caption` is specied, `title` is also used for the table's symbolic reference label.
`file` | name of file to write LaTeX code to. Specifying `file=""` will cause LaTeX code to just be printed to standard output rather than be stored in a permanent file.
`append` | specify `TRUE` to add code to an existing file
`rowlabel` | see `latex.default` (under the help file `latex`)
`rowsep` | if `html` is `TRUE`, instructs the function to use a horizontal line to separate variables from one another. Recommended if `brmsd` is `TRUE`. Ignored for LaTeX.
`middle.bold` | set to `TRUE` to have LaTeX use bold face for the middle quantile
`outer.size` | the font size for outer quantiles
`insert.bottom` | set to `FALSE` to suppress inclusion of definitions placed at the bottom of LaTeX tables. You can also specify a character string containing other text that overrides the automatic text. At present such text always appears in the main caption for LaTeX.
`legend.bottom` | set to `TRUE` to separate the table caption and legend. This will place table legends at the bottom of LaTeX tables.
`html` | set to `TRUE` to typeset with html
`mspecs` | list defining markup syntax for various languages, defaults to Hmisc `markupSpecs` which the user can use as a starting point for editing
`dcolumn` | see `latex`

### Value

a list. `plot.summaryM` returns the number of pages of plots that were made if using base graphics, or `plotly` objects created by `plotly::subplot` otherwise. If both categorical and continuous variables were plotted, the returned object is a list with two named elements `Categorical` and `Continuous` each containing `plotly` objects. Otherwise a `plotly` object is returned. The `latex` method returns attributes `legend` and `nstrata`.

### Side Effects

`plot.summaryM` creates a function `Key` and `Key2` in frame 0 that will draw legends, if base graphics are being used.

### Author(s)

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

### References

Harrell FE (2004): Statistical tables and plots using S and LaTeX. Document available from http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatReport/summary.pdf.

### See Also

`mChoice`, `label`, `dotchart3`, `print.char.matrix`, `update`, `formula`, `format.default`, `latex`, `latexTranslate`, `bpplt`, `tabulr`, `bpplotM`, `summaryP`

### Examples

    options(digits=3)
    set.seed(173)
    sex <- factor(sample(c("m","f"), 500, rep=TRUE))
    country <- factor(sample(c('US', 'Canada'), 500, rep=TRUE))
    age <- rnorm(500, 50, 5)
    sbp <- rnorm(500, 120, 12)
    label(sbp) <- 'Systolic BP'
    units(sbp) <- 'mmHg'
    treatment <- factor(sample(c("Drug","Placebo"), 500, rep=TRUE))
    treatment[1]
    sbp[1] <- NA

    # Generate a 3-choice variable; each of 3 variables has 5 possible levels
    symp <- c('Headache','Stomach Ache','Hangnail',
              'Muscle Ache','Depressed')
    symptom1 <- sample(symp, 500,TRUE)
    symptom2 <- sample(symp, 500,TRUE)
    symptom3 <- sample(symp, 500,TRUE)
    Symptoms <- mChoice(symptom1, symptom2, symptom3, label='Primary Symptoms')
    table(as.character(Symptoms))

    # Note: In this example, some subjects have the same symptom checked
    # multiple times; in practice these redundant selections would be NAs
    # mChoice will ignore these redundant selections

    f <- summaryM(age + sex + sbp + Symptoms ~ treatment, test=TRUE)
    f
    # trio of numbers represent 25th, 50th, 75th percentile
    print(f, long=TRUE)
    plot(f)    # first specify options(grType='plotly') to use plotly
    plot(f, conType='dot', prtest='P')
    bpplt()    # annotated example showing layout of bp plot

    # Produce separate tables by country
    f <- summaryM(age + sex + sbp + Symptoms ~ treatment + country,
                  groups='treatment', test=TRUE)
    f

    ## Not run:
    getHdata(pbc)
    s5 <- summaryM(bili + albumin + stage + protime + sex +
                   age + spiders ~ drug, data=pbc)

    print(s5, npct='both')
    # npct='both' : print both numerators and denominators
    plot(s5, which='categorical')
    Key(locator(1))  # draw legend at mouse click
    par(oma=c(3,0,0,0))  # leave outer margin at bottom
    plot(s5, which='continuous')  # see also bpplotM
    Key2()           # draw legend at lower left corner of plot
                     # oma= above makes this default key fit the page better

    options(digits=3)
    w <- latex(s5, npct='both', here=TRUE, file='')

    options(grType='plotly')
    pbc <- upData(pbc, moveUnits = TRUE)
    s <- summaryM(bili + albumin + alk.phos + copper + spiders + sex ~
                  drug, data=pbc, test=TRUE)
    html(s)
    a <- plot(s)
    a$Categorical
    a$Continuous
    plot(s, which='con')

    ## End(Not run)

[Package Hmisc version 4.3-0 Index]