# summary.formula

##### Summarize Data for Making Tables and Plots

`summary.formula`

summarizes the variables listed in an S formula,
computing descriptive statistics (including ones in a
user-specified function). The summary statistics may be passed to
`print`

methods, `plot`

methods for making annotated dot charts, and
`latex`

methods for typesetting tables using LaTeX.
`summary.formula`

has three methods for computing descriptive
statistics on univariate or multivariate responses, subsetted by
categories of other variables. The method of summarization is
specified in the parameter `method`

(see details below). For the
`response`

and `cross`

methods, the statistics used to
summarize the data
may be specified in a very flexible way (e.g., the geometric mean,
33rd percentile, Kaplan-Meier 2-year survival estimate, mixtures of
several statistics). The default summary statistic for these methods
is the mean (the proportion of positive responses for a binary
response variable). The `cross`

method is useful for creating data
frames which contain summary statistics that are passed to `trellis`

as raw data (to make multi-panel dot charts, for example). The
`print`

methods use the `print.char.matrix`

function to print boxed
tables.

The right 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`

in frame 0 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.summary.formula.reverse`

will try to lay out a multi-panel
graph to best fit all the individual dot charts for continuous
variables.

There is a subscripting method for objects created with
`method="response"`

.
This can be used to print or plot selected variables or summary statistics
where there would otherwise be too many on one page.

`cumcategory`

is a utility function useful when summarizing an ordinal
response variable. It converts such a variable having `k`

levels to a
matrix with `k-1`

columns, where column `i`

is a vector of zeros and
ones indicating that the categorical response is in level `i+1`

or
greater. When the left hand side of `formula`

is `cumcategory(y)`

,
the default `fun`

will summarize it by computing all of the relevant
cumulative proportions.

Functions `conTestkw`

, `catTestchisq`

, `ordTestpo`

are
the default statistical test functions for `summary.formula`

.
These defaults are: Wilcoxon-Kruskal-Wallis test for continuous
variables, Pearson chi-square test for categorical variables, and the
likelihood ratio chi-square test from the proportional odds model for
ordinal variables. These three functions serve also as templates for
the user to create her own testing functions that are self-defining in
terms of how the results are printed or rendered in LaTeX, or plotted.

##### Usage

```
# S3 method for formula
summary(formula, data=NULL, subset=NULL,
na.action=NULL, fun = NULL,
method = c("response", "reverse", "cross"),
overall = method == "response" | method == "cross",
continuous = 10, na.rm = TRUE, na.include = method != "reverse",
g = 4, 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 = if (method == "reverse") 100
else 0,
test = FALSE, conTest = conTestkw, catTest = catTestchisq,
ordTest = ordTestpo, …)
```# S3 method for summary.formula.response
[(x, i, j, drop=FALSE)

# S3 method for summary.formula.response
print(x, vnames=c('labels','names'), prUnits=TRUE,
abbreviate.dimnames=FALSE,
prefix.width, min.colwidth, formatArgs=NULL, …)

# S3 method for summary.formula.response
plot(x, which = 1, vnames = c('labels','names'), xlim, xlab,
pch = c(16, 1, 2, 17, 15, 3, 4, 5, 0), superposeStrata = TRUE,
dotfont = 1, add = FALSE, reset.par = TRUE, main, subtitles = TRUE,
…)

# S3 method for summary.formula.response
latex(object, title = first.word(deparse(substitute(object))), caption,
trios, vnames = c('labels', 'names'), prn = TRUE, prUnits = TRUE,
rowlabel = '', cdec = 2, ncaption = TRUE, …)

# S3 method for summary.formula.reverse
print(x, digits, prn = any(n != N), pctdig = 0,
what=c('%', 'proportion'),
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, …)

# S3 method for summary.formula.reverse
plot(x, vnames = c('labels', 'names'), what = c('proportion', '%'),
which = c('both', 'categorical', 'continuous'),
xlim = if(what == 'proportion') c(0,1)
else c(0,100),
xlab = if(what=='proportion') 'Proportion'
else 'Percentage',
pch = c(16, 1, 2, 17, 15, 3, 4, 5, 0), exclude1 = TRUE,
dotfont = 1, main,
prtest = c('P', 'stat', 'df', 'name'), pdig = 3, eps = 0.001,
conType = c('dot', 'bp', 'raw'), cex.means = 0.5, …)

# S3 method for summary.formula.reverse
latex(object, title = first.word(deparse(substitute(object))), digits,
prn = any(n != N), pctdig = 0, what=c('%', 'proportion'),
npct = c("numerator", "both", "denominator", "slash", "none"),
npct.size = 'scriptsize', Nsize = "scriptsize", exclude1 = TRUE,
vnames=c("labels", "names"), prUnits = TRUE, middle.bold = FALSE,
outer.size = "scriptsize", caption, rowlabel = "",
insert.bottom = TRUE, dcolumn = FALSE, formatArgs=NULL, round = NULL,
prtest = c('P', 'stat', 'df', 'name'), prmsd = FALSE,
msdsize = NULL, long = dotchart, pdig = 3, eps = 0.001,
auxCol = NULL, dotchart=FALSE, …)

# S3 method for summary.formula.cross
print(x, twoway = nvar == 2, prnmiss = any(stats$Missing > 0), prn = TRUE,
abbreviate.dimnames = FALSE, prefix.width = max(nchar(v)),
min.colwidth, formatArgs = NULL, …)

# S3 method for summary.formula.cross
latex(object, title = first.word(deparse(substitute(object))),
twoway = nvar == 2, prnmiss = TRUE, prn = TRUE,
caption=attr(object, "heading"), vnames=c("labels", "names"),
rowlabel="", …)

stratify(…, na.group = FALSE, shortlabel = TRUE)

# S3 method for summary.formula.cross
formula(x, …)

cumcategory(y)

conTestkw(group, x)
catTestchisq(tab)
ordTestpo(group, x)

##### Arguments

- formula
An R formula with additive effects. For

`method="response"`

or`"cross"`

, the dependent variable has the usual connotation. For`method="reverse"`

, the dependent variable is what is usually thought of as an independent variable, and it is one that is used to stratify all of the right hand side variables. For`method="response"`

(only), 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 major column groupings in the resulting table, or as more response columns for plotting. If`formula`

has no dependent variable`method="reverse"`

is the only legal value and so`method`

defaults to`"reverse"`

in this case.- x
an object created by

`summary.formula`

. For`conTestkw`

a numeric vector, and for`ordTestpo`

, a numeric or factor variable that can be considered ordered- y
a numeric, character, category, or factor vector for

`cumcategory`

. Is converted to a categorical variable is needed.- drop
logical. If

`TRUE`

the result is coerced to the lowest possible dimension.- 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 summarizing data in each cell. Default is to take the mean of each column of the possibly multivariate response variable. You can specify

`fun="%"`

to compute percentages (100 times the mean of a series of logical or binary variables). User--specified functions can also return a matrix. For example, you might compute quartiles on a bivariate response. Does not apply to`method="reverse"`

.- method
The default is

`"response"`

, in which case the response variable may be multivariate and any number of statistics may be used to summarize them. Here the responses are summarized separately for each of any number of independent variables. Continuous independent variables (see the`continuous`

parameter below) are automatically stratified into`g`

(see below) quantile groups (if you want to control the discretization for selected variables, use the`cut2`

function on them). Otherwise, the data are subsetted by all levels of discrete right hand side variables. For multivariate responses, subjects are considered to be missing if any of the columns is missing.The

`method="reverse"`

option is typically used to make baseline characteristic tables, for example. The single left hand side variable must be categorical (e.g., treatment), and the right hand side variables are broken down one at a time by the "dependent" variable. Continuous variables are described by three quantiles (quartiles by default) along with outer quantiles (used only for scaling x-axes when plotting quartiles; all are used when plotting box-percentile plots), and categorical ones are described by counts and percentages. If there is no left hand side variable,`summary`

assumes that there is only one group in the data, so that only one column of summaries will appear. If there is no dependent variable in`formula`

,`method`

defaults to`"reverse"`

automatically.The

`method="cross"`

option allows for a multivariate dependent variable and for up to three independents. Continuous independent variables (those with at least`continuous`

unique values) are automatically divided into`g`

quantile groups. The independents are cross-classified, and marginal statistics may optionally be computed. The output of`summary.formula`

in this case is a data frame containing the independent variable combinations (with levels of`"All"`

corresponding to marginals) and the corresponding summary statistics in the matrix`S`

. The output data frame is suitable for direct use in`trellis`

. The`print`

and`latex`

typesetting methods for this method allows for a special two-way format if there are two right hand variables.- overall
For

`method="reverse"`

, setting`overall=TRUE`

makes a new column with overall statistics for the whole sample. For`method="cross"`

,`overall=TRUE`

(the default) results in all marginal statistics being computed. For`trellis`

displays (usually multi-panel dot plots), these marginals just form other categories. For`"response"`

, the default is`overall=TRUE`

, causing a final row of global summary statistics to appear in tables and dot charts. 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.rm
`TRUE`

(the default) to exclude`NA`

s before passing data to`fun`

to compute statistics,`FALSE`

otherwise.`na.rm=FALSE`

is useful if the response variable is a matrix and you do not wish to exclude a row of the matrix if any of the columns in that row are`NA`

.`na.rm`

also applies to summary statistic functions such as`smean.cl.normal`

. For these`na.rm`

defaults to`TRUE`

unlike built-in functions.- na.include
for

`method="response"`

, set`na.include=FALSE`

to exclude missing values from being counted as their own category when subsetting the response(s) by levels of a categorical variable. For`method="reverse"`

set`na.include=TRUE`

to keep missing values of categorical variables from being excluded from the table.- g
number of quantile groups to use when variables are automatically categorized with

`method="response"`

or`"cross"`

using`cut2`

- nmin
if fewer than

`nmin`

observations exist in a category for`"response"`

(over all strata combined), that category will be ignored. For`"reverse"`

, 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
applies if

`method="reverse"`

. 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),`statname`

(statistic name),`namefun`

(`"chisq", "fstat"`

), 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

`summary.formula`

these are optional arguments for`cut2`

when variables are automatically categorized. For`plot`

methods these arguments are passed to`dotchart2`

. 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`

. One of the most important of these is`file`

. Specifying`file=""`

will cause LaTeX code to just be printed to standard output rather than be stored in a permanent file.- object
an object created by

`summary.formula`

- quant
vector of quantiles to use for summarizing data with

`method="reverse"`

. This 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.- 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. For

`method="response"`

the characters correspond to levels of the`stratify`

variable if`superposeStrata=TRUE`

, and if no`strata`

are used or if`superposeStrata=FALSE`

, the`pch`

vector corresponds to the`which`

argument for`method="response"`

.- superposeStrata
If

`stratify`

was used, set`superposeStrata=FALSE`

to make separate dot charts for each level of the`stratification`

variable, for`method='response'`

. The default is to superposition all strata on one dot chart.- dotfont
font for plotting points

- reset.par
set to

`FALSE`

to suppress the restoring of the old par values in`plot.summary.formula.response`

- 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.- 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. For`method="cross"`

the default is to always print`N`

.- prnmiss
set to

`FALSE`

to suppress printing counts of missing values for`"cross"`

- what
for

`method="reverse"`

specifies whether proportions or percentages are to be plotted- pctdig
number of digits to the right of the decimal place for printing percentages. The default is zero, so percents will be rounded to the nearest percent.

- 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,`"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,

`method="reverse"`

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

`method="reverse"`

tables- prtest
a vector of test statistic components to print if

`test=TRUE`

was in effect when`summary.formula`

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 for`method='reverse'`

.- round
for

`print.summary.formula.reverse`

and`latex.summary.formula.reverse`

specify`round`

to round the quantiles and optional mean and standard deviation to`round`

digits after the decimal point- prmsd
set to

`TRUE`

to print mean and SD after the three quantiles, for continuous variables with`method="reverse"`

- 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 to specify an alternate LaTeX font size.- 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 (for`method="reverse"`

with`print`

and`latex`

methods)- 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.summary.formula.reverse`

. 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.- twoway
for

`method="cross"`

with two right hand side variables,`twoway`

controls whether the resulting table will be printed in enumeration format or as a two-way table (the default)- which
For

`method="response"`

specifies the sequential number or a vector of subscripts of statistics to plot. If you had any`stratify`

variables, these are counted as if more statistics were computed. For`method="reverse"`

specifies whether to plot results for categorical variables, continuous variables, or both (the default).- conType
For plotting

`method="reverse"`

plots for continuous variables, dot plots showing quartiles are drawn by default. Specify`conType='bp'`

to draw box-percentile plots using all the quantiles in`quant`

except the outermost ones. 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 left-hand-side group is less than or equal to`nmin`

.- cex.means
character size for means in box-percentile plots; default is .5

- xlim
vector of length two specifying x-axis limits. For

`method="reverse"`

, 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

- add
set to

`TRUE`

to add to an existing plot- main
a main title. For

`method="reverse"`

this applies only to the plot for categorical variables.- subtitles
set to

`FALSE`

to suppress automatic subtitles- 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.- trios
If for

`method="response"`

you summarized the response(s) by using three quantiles, specify`trios=TRUE`

or`trios=v`

to group each set of three statistics into one column for`latex`

output, using the format a B c, where the outer quantiles are in smaller font (`scriptsize`

). For`trios=TRUE`

, the overall column names are taken from the column names of the original data matrix. To give new column names, specify`trios=v`

, where`v`

is a vector of column names, of length`m/3`

, where`m`

is the original number of columns of summary statistics.- rowlabel
see

`latex.default`

(under the help file`latex`

)- cdec
number of decimal places to the right of the decimal point for

`latex`

. This value should be a scalar (which will be properly replicated), or a vector with length equal to the number of columns in the table. For`"response"`

tables, this length does not count the column for`N`

.- ncaption
set to

`FALSE`

to not have`latex.summary.formula.response`

put sample sizes in captions- i
a vector of integers, or character strings containing variable names to subset on. Note that each row subsetted on in an

`summary.formula.reverse`

object subsets on all the levels that make up the corresponding variable (automatically).- j
a vector of integers representing column numbers

- middle.bold
set to

`TRUE`

to have LaTeX use bold face for the middle quantile for`method="reverse"`

- outer.size
the font size for outer quantiles for

`"reverse"`

tables- insert.bottom
set to

`FALSE`

to suppress inclusion of definitions placed at the bottom of LaTeX tables for`method="reverse"`

- dcolumn
see

`latex`

- na.group
set to

`TRUE`

to have missing stratification variables given their own category (`NA`

)- shortlabel
set to

`FALSE`

to include stratification variable names and equal signs in labels for strata levels- dotchart
set to

`TRUE`

to output a dotchart in the latex table being generated.- group
for

`conTest`

and`ordTest`

, a numeric or factor variable with length the same as`x`

- tab
for

`catTest`

, a frequency table such as that created by`table()`

##### Value

`summary.formula`

returns a data frame or list depending on
`method`

. `plot.summary.formula.reverse`

returns the number
of pages of plots that were made.

##### Side Effects

`plot.summary.formula.reverse`

creates a function `Key`

and
`Key2`

in frame 0 that will draw legends.

##### 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`

, `smean.sd`

, `summarize`

,
`label`

, `strata`

, `dotchart2`

,
`print.char.matrix`

, `update`

,
`formula`

, `cut2`

, `llist`

,
`format.default`

, `latex`

,
`latexTranslate`

`bpplt`

,
`summaryM`

, `summary`

##### Examples

```
# NOT RUN {
options(digits=3)
set.seed(173)
sex <- factor(sample(c("m","f"), 500, rep=TRUE))
age <- rnorm(500, 50, 5)
treatment <- factor(sample(c("Drug","Placebo"), 500, rep=TRUE))
# 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(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
#Frequency table sex*treatment, sex*Symptoms
summary(sex ~ treatment + Symptoms, fun=table)
# could also do summary(sex ~ treatment +
# mChoice(symptom1,symptom2,symptom3), fun=table)
#Compute mean age, separately by 3 variables
summary(age ~ sex + treatment + Symptoms)
f <- summary(treatment ~ age + sex + Symptoms, method="reverse", test=TRUE)
f
# trio of numbers represent 25th, 50th, 75th percentile
print(f, long=TRUE)
plot(f)
plot(f, conType='bp', prtest='P')
bpplt() # annotated example showing layout of bp plot
#Compute predicted probability from a logistic regression model
#For different stratifications compute receiver operating
#characteristic curve areas (C-indexes)
predicted <- plogis(.4*(sex=="m")+.15*(age-50))
positive.diagnosis <- ifelse(runif(500)<=predicted, 1, 0)
roc <- function(z) {
x <- z[,1];
y <- z[,2];
n <- length(x);
if(n<2)return(c(ROC=NA));
n1 <- sum(y==1);
c(ROC= (mean(rank(x)[y==1])-(n1+1)/2)/(n-n1) );
}
y <- cbind(predicted, positive.diagnosis)
options(digits=2)
summary(y ~ age + sex, fun=roc)
options(digits=3)
summary(y ~ age + sex, fun=roc, method="cross")
#Use stratify() to produce a table in which time intervals go down the
#page and going across 3 continuous variables are summarized using
#quartiles, and are stratified by two treatments
set.seed(1)
d <- expand.grid(visit=1:5, treat=c('A','B'), reps=1:100)
d$sysbp <- rnorm(100*5*2, 120, 10)
label(d$sysbp) <- 'Systolic BP'
d$diasbp <- rnorm(100*5*2, 80, 7)
d$diasbp[1] <- NA
d$age <- rnorm(100*5*2, 50, 12)
g <- function(y) {
N <- apply(y, 2, function(w) sum(!is.na(w)))
h <- function(x) {
qu <- quantile(x, c(.25,.5,.75), na.rm=TRUE)
names(qu) <- c('Q1','Q2','Q3')
c(N=sum(!is.na(x)), qu)
}
w <- as.vector(apply(y, 2, h))
names(w) <- as.vector( outer(c('N','Q1','Q2','Q3'), dimnames(y)[[2]],
function(x,y) paste(y,x)))
w
}
#Use na.rm=FALSE to count NAs separately by column
s <- summary(cbind(age,sysbp,diasbp) ~ visit + stratify(treat),
na.rm=FALSE, fun=g, data=d)
#The result is very wide. Re-do, putting treatment vertically
x <- with(d, factor(paste('Visit', visit, treat)))
summary(cbind(age,sysbp,diasbp) ~ x, na.rm=FALSE, fun=g, data=d)
#Compose LaTeX code directly
g <- function(y) {
h <- function(x) {
qu <- format(round(quantile(x, c(.25,.5,.75), na.rm=TRUE),1),nsmall=1)
paste('{\\scriptsize(',sum(!is.na(x)),
')} \\hfill{\\scriptsize ', qu[1], '} \\textbf{', qu[2],
'} {\\scriptsize ', qu[3],'}', sep='')
}
apply(y, 2, h)
}
s <- summary(cbind(age,sysbp,diasbp) ~ visit + stratify(treat),
na.rm=FALSE, fun=g, data=d)
# latex(s, prn=FALSE)
## need option in latex to not print n
#Put treatment vertically
s <- summary(cbind(age,sysbp,diasbp) ~ x, fun=g, data=d, na.rm=FALSE)
# latex(s, prn=FALSE)
#Plot estimated mean life length (assuming an exponential distribution)
#separately by levels of 4 other variables. Repeat the analysis
#by levels of a stratification variable, drug. Automatically break
#continuous variables into tertiles.
#We are using the default, method='response'
# }
# NOT RUN {
life.expect <- function(y) c(Years=sum(y[,1])/sum(y[,2]))
attach(pbc)
S <- Surv(follow.up.time, death)
s2 <- summary(S ~ age + albumin + ascites + edema + stratify(drug),
fun=life.expect, g=3)
#Note: You can summarize other response variables using the same
#independent variables using e.g. update(s2, response~.), or you
#can change the list of independent variables using e.g.
#update(s2, response ~.- ascites) or update(s2, .~.-ascites)
#You can also print, typeset, or plot subsets of s2, e.g.
#plot(s2[c('age','albumin'),]) or plot(s2[1:2,])
s2 # invokes print.summary.formula.response
#Plot results as a separate dot chart for each of the 3 strata levels
par(mfrow=c(2,2))
plot(s2, cex.labels=.6, xlim=c(0,40), superposeStrata=FALSE)
#Typeset table, creating s2.tex
w <- latex(s2, cdec=1)
#Typeset table but just print LaTeX code
latex(s2, file="") # useful for Sweave
#Take control of groups used for age. Compute 3 quartiles for
#both cholesterol and bilirubin (excluding observations that are missing
#on EITHER ONE)
age.groups <- cut2(age, c(45,60))
g <- function(y) apply(y, 2, quantile, c(.25,.5,.75))
y <- cbind(Chol=chol,Bili=bili)
label(y) <- 'Cholesterol and Bilirubin'
#You can give new column names that are not legal S names
#by enclosing them in quotes, e.g. 'Chol (mg/dl)'=chol
s <- summary(y ~ age.groups + ascites, fun=g)
par(mfrow=c(1,2), oma=c(3,0,3,0)) # allow outer margins for overall
for(ivar in 1:2) { # title
isub <- (1:3)+(ivar-1)*3 # *3=number of quantiles/var.
plot(s3, which=isub, main='',
xlab=c('Cholesterol','Bilirubin')[ivar],
pch=c(91,16,93)) # [, closed circle, ]
}
mtext(paste('Quartiles of', label(y)), adj=.5, outer=TRUE, cex=1.75)
#Overall (outer) title
prlatex(latex(s3, trios=TRUE))
# trios -> collapse 3 quartiles
#Summarize only bilirubin, but do it with two statistics:
#the mean and the median. Make separate tables for the two randomized
#groups and make plots for the active arm.
g <- function(y) c(Mean=mean(y), Median=median(y))
for(sub in c("D-penicillamine", "placebo")) {
ss <- summary(bili ~ age.groups + ascites + chol, fun=g,
subset=drug==sub)
cat('\n',sub,'\n\n')
print(ss)
if(sub=='D-penicillamine') {
par(mfrow=c(1,1))
plot(s4, which=1:2, dotfont=c(1,-1), subtitles=FALSE, main='')
#1=mean, 2=median -1 font = open circle
title(sub='Closed circle: mean; Open circle: median', adj=0)
title(sub=sub, adj=1)
}
w <- latex(ss, append=TRUE, fi='my.tex',
label=if(sub=='placebo') 's4b' else 's4a',
caption=paste(label(bili),' {\\em (',sub,')}', sep=''))
#Note symbolic labels for tables for two subsets: s4a, s4b
prlatex(w)
}
#Now consider examples in 'reverse' format, where the lone dependent
#variable tells the summary function how to stratify all the
#'independent' variables. This is typically used to make tables
#comparing baseline variables by treatment group, for example.
s5 <- summary(drug ~ bili + albumin + stage + protime + sex +
age + spiders,
method='reverse')
#To summarize all variables, use summary(drug ~., data=pbc)
#To summarize all variables with no stratification, use
#summary(~a+b+c) or summary(~.,data=\dots)
options(digits=1)
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')
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)
# creates s5.tex
#Turn to a different dataset and do cross-classifications on possibly
#more than one independent variable. The summary function with
#method='cross' produces a data frame containing the cross-
#classifications. This data frame is suitable for multi-panel
#trellis displays, although `summarize' works better for that.
attach(prostate)
size.quartile <- cut2(sz, g=4)
bone <- factor(bm,labels=c("no mets","bone mets"))
s7 <- summary(ap>1 ~ size.quartile + bone, method='cross')
#In this case, quartiles are the default so could have said sz + bone
options(digits=3)
print(s7, twoway=FALSE)
s7 # same as print(s7)
w <- latex(s7, here=TRUE) # Make s7.tex
library(trellis,TRUE)
invisible(ps.options(reset=TRUE))
trellis.device(postscript, file='demo2.ps')
dotplot(S ~ size.quartile|bone, data=s7, #s7 is name of summary stats
xlab="Fraction ap>1", ylab="Quartile of Tumor Size")
#Can do this more quickly with summarize:
# s7 <- summarize(ap>1, llist(size=cut2(sz, g=4), bone), mean,
# stat.name='Proportion')
# dotplot(Proportion ~ size | bone, data=s7)
summary(age ~ stage, method='cross')
summary(age ~ stage, fun=quantile, method='cross')
summary(age ~ stage, fun=smean.sd, method='cross')
summary(age ~ stage, fun=smedian.hilow, method='cross')
summary(age ~ stage, fun=function(x) c(Mean=mean(x), Median=median(x)),
method='cross')
#The next statements print real two-way tables
summary(cbind(age,ap) ~ stage + bone,
fun=function(y) apply(y, 2, quantile, c(.25,.75)),
method='cross')
options(digits=2)
summary(log(ap) ~ sz + bone,
fun=function(y) c(Mean=mean(y), quantile(y)),
method='cross')
#Summarize an ordered categorical response by all of the needed
#cumulative proportions
summary(cumcategory(disease.severity) ~ age + sex)
# }
```

*Documentation reproduced from package Hmisc, version 4.0-3, License: GPL (>= 2)*