summary.rms  R Documentation 
summary.rms
forms a summary of the effects of each
factor. When summary
is used to estimate odds or hazard ratios for
continuous variables, it allows the levels of interacting factors to be
easily set, as well as allowing the user to choose the interval for the
effect. This method of estimating effects allows for nonlinearity in
the predictor. Factors requiring multiple parameters are handled, as
summary
obtains predicted values at the needed points and takes
differences. By default, interquartile range effects (odds ratios,
hazards ratios, etc.) are printed for continuous factors, and all
comparisons with the reference level are made for categorical factors.
print.summary.rms
prints the results, latex.summary.rms
and html.summary.rms
typeset the results, and plot.summary.rms
plots shaded confidence bars to display the results graphically.
The longest confidence bar on each page is labeled with confidence levels
(unless this bar has been ignored due to clip
). By default, the
following confidence levels are all shown: .9, .95, and .99, using
blue of different transparencies. The plot
method currently
ignores bootstrap and Bayesian highest posterior density intervals but approximates
intervals based on standard errors. The html
method is for use
with R Markdown using html.
The print
method will call the latex
or html
method
if options(prType=)
is set to "latex"
or "html"
.
For "latex"
printing through print()
, the LaTeX table
environment is turned off.
If usebootcoef=TRUE
and the fit was run through bootcov
,
the confidence intervals are bootstrap nonparametric percentile
confidence intervals, basic bootstrap, or BCa intervals, obtained on contrasts
evaluated on all bootstrap samples.
If options(grType='plotly')
is in effect and the plotly
package is installed, plot
is used instead of base graphics to
draw the point estimates and confidence limits when the plot
method for summary
is called. Colors and other graphical
arguments to plot.summary
are ignored in this case. Various
special effects are implemented such as only drawing 0.95 confidence
limits by default but including a legend that allows the other CLs to be
activated. Hovering over point estimates shows adjustment values if
there are any. nbar
is not implemented for plotly
.
## S3 method for class 'rms' summary(object, ..., ycut=NULL, est.all=TRUE, antilog, conf.int=.95, abbrev=FALSE, vnames=c("names","labels"), conf.type=c('individual','simultaneous'), usebootcoef=TRUE, boot.type=c("percentile","bca","basic"), posterior.summary=c('mean', 'median', 'mode'), verbose=FALSE) ## S3 method for class 'summary.rms' print(x, ..., table.env=FALSE) ## S3 method for class 'summary.rms' latex(object, title, table.env=TRUE, ...) ## S3 method for class 'summary.rms' html(object, digits=4, dec=NULL, ...) ## S3 method for class 'summary.rms' plot(x, at, log=FALSE, q=c(0.9, 0.95, 0.99), xlim, nbar, cex=1, nint=10, cex.main=1, clip=c(1e30,1e30), main, col=rgb(red=.1,green=.1,blue=.8,alpha=c(.1,.4,.7)), col.points=rgb(red=.1,green=.1,blue=.8,alpha=1), pch=17, lwd=if(length(q) == 1) 3 else 2 : (length(q) + 1), digits=4, ...)
object 
a 
... 
For Also represents other arguments to pass to 
ycut 
must be specified if the fit is a partial proportional odds model. Specifies the single value of the response variable used to estimate ycutspecific regression effects, e.g., odds ratios 
est.all 
Set to 
antilog 
Set to 
conf.int 
Defaults to 
abbrev 
Set to 
vnames 
Set to 
conf.type 
The default type of confidence interval computed for a given
individual (1 d.f.) contrast is a pointwise confidence interval. Set

usebootcoef 
If 
boot.type 
set to 
posterior.summary 
set to 
verbose 
set to 
x 
result of 
title 

table.env 
see 
digits,dec 
for 
at 
vector of coordinates at which to put tick mark labels on the main axis. If

log 
Set to 
q 
scalar or vector of confidence coefficients to depict 
xlim 
Xaxis limits for 
nbar 
Sets up plot to leave room for 
cex 

nint 
Number of tick mark numbers for 
cex.main 

clip 
confidence limits outside the interval 
main 
main title. Default is inferred from the model and value of 
col 
vector of colors, one per value of 
col.points 
color for points estimates 
pch 
symbol for point estimates. Default is solid triangle. 
lwd 
line width for confidence intervals, corresponding to

For summary.rms
, a matrix of class summary.rms
with rows corresponding to factors in
the model and columns containing the low and high values for the effects,
the range for the effects, the effect point estimates (difference in
predicted values for high and low factor values), the standard error
of this effect estimate, and the lower and upper confidence limits.
If fit$scale.pred
has a second level, two rows appear for each factor,
the second corresponding to antiâ€“logged effects. Nonâ€“categorical factors
are stored first, and effects for any categorical factors are stored at
the end of the returned matrix. scale.pred
and adjust
. adjust
is a character string containing levels of adjustment variables, if
there are any interactions. Otherwise it is "".
latex.summary.rms
returns an object of class c("latex","file")
.
It requires the latex
function in Hmisc.
Frank Harrell
Hui Nian
Department of Biostatistics, Vanderbilt University
fh@fharrell.com
datadist
, rms
, rms.trans
,
rmsMisc
,
Misc
, pretty
, contrast.rms
n < 1000 # define sample size set.seed(17) # so can reproduce the results age < rnorm(n, 50, 10) blood.pressure < rnorm(n, 120, 15) cholesterol < rnorm(n, 200, 25) sex < factor(sample(c('female','male'), n,TRUE)) label(age) < 'Age' # label is in Hmisc label(cholesterol) < 'Total Cholesterol' label(blood.pressure) < 'Systolic Blood Pressure' label(sex) < 'Sex' units(cholesterol) < 'mg/dl' # uses units.default in Hmisc units(blood.pressure) < 'mmHg' # Specify population model for log odds that Y=1 L < .4*(sex=='male') + .045*(age50) + (log(cholesterol  10)5.2)*(2*(sex=='female') + 2*(sex=='male')) # Simulate binary y to have Prob(y=1) = 1/[1+exp(L)] y < ifelse(runif(n) < plogis(L), 1, 0) ddist < datadist(age, blood.pressure, cholesterol, sex) options(datadist='ddist') fit < lrm(y ~ blood.pressure + sex * (age + rcs(cholesterol,4))) s < summary(fit) # Estimate effects using default ranges # Gets odds ratio for age=3rd quartile # compared to 1st quartile ## Not run: latex(s) # Use LaTeX to print nice version latex(s, file="") # Just write LaTeX code to console html(s) # html/LaTeX to console for knitr # Or: options(prType='latex') summary(fit) # prints with LaTeX, table.env=FALSE options(prType='html') summary(fit) # prints with html ## End(Not run) summary(fit, sex='male', age=60) # Specify ref. cell and adjustment val summary(fit, age=c(50,70)) # Estimate effect of increasing age from # 50 to 70 s < summary(fit, age=c(50,60,70)) # Increase age from 50 to 70, adjust to # 60 when estimating effects of other factors #Could have omitted datadist if specified 3 values for all noncategorical #variables (1 value for categorical ones  adjustment level) plot(s, log=TRUE, at=c(.1,.5,1,1.5,2,4,8)) options(datadist=NULL)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.