[Rd] Rd syntax error detected in CRAN daily checks

Frank E Harrell Jr f.harrell at vanderbilt.edu
Thu Sep 9 18:49:43 CEST 2004

Please forgive me if you already received this.  I had an e-mail sending 
glitch this morning.

http://cran.r-project.org/src/contrib/checkSummary.html reported an 
error in Design.trans.Rd

* checking Rd files ... ERROR
Rd files with syntax errors:
     unterminated section 'alias'

The .Rd file is attached.  It begins with

Design Special Transformation Functions
This is a series of functions (\code{asis}, \code{pol}, \code{lsp},

No error is detected when I run CMD CHECK using the latest R-devel 
locally, using the 5 Sep 04 version.  I would appreciate assistance. 
Please cc: me because I get r-devel in digest format.

Thanks -Frank
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University
-------------- next part --------------
Design Special Transformation Functions
This is a series of functions (\code{asis}, \code{pol}, \code{lsp}, \code{rcs}, \code{catg},
\code{scored}, \code{strat}, \code{matrx}, and \code{\%ia\%}) that set up special attributes 
(such as
knots and nonlinear term indicators) that are carried through to fits
(using for example \code{lrm},\code{cph}, \code{ols}, \code{psm}). \code{anova.Design}, \code{summary.Design},
\code{plot.Design}, \code{survplot}, \code{fastbw}, \code{validate}, \code{specs}, 
\code{which.influence}, \code{nomogram.Design} and \code{latex.Design} use these
attributes to automate certain analyses (e.g., automatic tests of linearity
for each predictor are done by \code{anova.Design}). Many of the functions
are called implicitly.  Some S functions such as \code{ns} derive data-dependent
transformations that are not "remembered" when predicted values are
later computed, so the predictions will be incorrect. The functions listed
here solve that problem. 

\code{asis} is the identity transformation, \code{pol} is an ordinary (non-orthogonal) polynomial, \code{rcs} is
a linear tail-restricted cubic spline function (natural spline, for which the
\code{rcspline.eval} function generates the design matrix),
\code{catg} is for a categorical
variable, \code{scored} is for an ordered categorical
variable, \code{strat} is for a stratification factor
in a Cox model, \code{matrx} is for a matrix predictor, and \code{\%ia\%} represents
restricted interactions in which products involving nonlinear effects on both
variables are not included in the model.  \code{asis, catg, scored, matrx} are seldom invoked
explicitly by the user (only to specify \code{label} or \code{name}, usually).

In the list below, functions \code{asis} through \code{strat} can have
arguments \code{x, parms, label, name} except that \code{parms} does not
apply to \code{asis, matrx, strat}.
\%ia\%(x1, x2)
asis(x, parms, label, name)
matrx(x, label, name)
pol(x, parms, label, name)
lsp(x, parms, label, name)
rcs(x, parms, label, name)
catg(x, parms, label, name)
scored(x, parms, label, name)
strat(x, label, name)
\%ia\%(x1, x2)
a predictor variable (or a function of one).  If you specify e.g.
\code{pol(pmin(age,10),3)}, a cubic polynomial will be fitted in \code{pmin(age,10)}
(\code{pmin} is the S vector element--by--element function).
The predictor will be labeled \code{age} in the output, and plots with have
\code{age} in its original units on the axes. If you use a function such as
\code{pmin}, the predictor is taken as the first argument, and other arguments
must be defined in the frame in effect when predicted values, etc., are
parameters of transformation (e.g. number or location of knots).
For \code{pol} the argument is the order of the polynomial,
e.g. \code{2} for quadratic (the usual default). For \code{lsp} it is a
vector of knot locations (\code{lsp} will not estimate knot locations).
For \code{rcs} it is the
number of knots (if scalar), or vector of knot locations (if \code{>2} elements).
The default number is the \code{nknots} system option if \code{parms} is not given.
If the number of knots is given,
locations are computed for that number of knots.
For \code{catg}, \code{parms} is the
category labels (not needed if variable is an S category or factor variable). If
omitted, \code{catg} will use \code{unique(x)}, or \code{levels(x)} if \code{x} is a \code{category}
or a \code{factor}.
For \code{scored}, \code{parms} is a
vector of unique values of variable (uses \code{unique(x)} by default).
This is not needed if \code{x} is an S \code{ordered} variable.
For \code{strat}, \code{parms} is the category labels (not needed if variable is an S category variable). If
omitted, will use \code{unique(x)}, or \code{levels(x)} if \code{x} is
\code{category} or \code{factor}.
\code{parms} is not used for \code{matrix}.
label of predictor for plotting (default = \code{"label"} attribute or variable
Name to use for predictor in model. Default is name of argument to
\item{x2}{two continuous variables for which to form a
  non-doubly-nonlinear interaction}
\item{\dots}{a variety of things}
Frank Harrell\cr
Department of Biostatistics, Vanderbilt University\cr
f.harrell at vanderbilt.edu
\code{\link[Hmisc]{rcspline.eval}}, \code{\link[Hmisc]{rcspline.restate}}, \code{\link{Design}}, \code{\link{cph}}, \code{\link{lrm}}, \code{\link{ols}}, \code{\link{datadist}}
options(knots=4, poly.degree=2)
country <- factor(country.codes)
blood.pressure <- cbind(sbp=systolic.bp, dbp=diastolic.bp)
fit <- lrm(Y ~ sqrt(x1)*rcs(x2) + rcs(x3,c(5,10,15)) + 
       lsp(x4,c(10,20)) + country + blood.pressure + poly(age,2))
# sqrt(x1) is an implicit asis variable, but limits of x1, not sqrt(x1)
#       are used for later plotting and effect estimation
# x2 fitted with restricted cubic spline with 4 default knots
# x3 fitted with r.c.s. with 3 specified knots
# x4 fitted with linear spline with 2 specified knots
# country is an implied catg variable
# blood.pressure is an implied matrx variable
# since poly is not a Design function (pol is), it creates a
#       matrx type variable with no automatic linearity testing
#       or plotting
f1 <- lrm(y ~ rcs(x1) + rcs(x2) + rcs(x1) \%ia\% rcs(x2))
# \%ia\% restricts interactions. Here it removes terms nonlinear in
# both x1 and x2
f2 <- lrm(y ~ rcs(x1) + rcs(x2) + x1 \%ia\% rcs(x2))
# interaction linear in x1
f3 <- lrm(y ~ rcs(x1) + rcs(x2) + x1 \%ia\% x2)
# simple product interaction (doubly linear)
# Use x1 \%ia\% x2 instead of x1:x2 because x1 \%ia\% x2 triggers
# anova to pool x1*x2 term into x1 terms to test total effect
# of x1
\concept{logistic regression model}

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