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, inter-quartile 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. When using html with Quarto or RMarkdown,
results='asis' need not be written in the chunk header.
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 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 summary.rms
print(x, ..., table.env=FALSE)
# S3 method for summary.rms
latex(object, title, table.env=TRUE, ...)
# S3 method for summary.rms
html(object, digits=4, dec=NULL, ...)
# S3 method for 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,
declim=4, ...)
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 anti--logged effects. Non--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.
a rms fit object. Either options(datadist) should have
been set before the fit, or datadist() and
options(datadist) run before summary. For latex is
the result of summary.
For summary, omit list of variables to estimate effects for all
predictors. Use a list
of variables of the form age, sex to estimate using default
ranges. Specify age=50 for example to adjust age to 50 when testing
other factors (this will only matter for factors that interact with age).
Specify e.g. age=c(40,60) to estimate the effect of increasing age from
40 to 60. Specify age=c(40,50,60) to let age range from 40 to 60 and
be adjusted to 50 when testing other interacting factors. For category
factors, a single value specifies the reference cell and the adjustment
value. For example, if treat has levels "a", "b" and
"c" and treat="b" is given to summary,
treatment a will be compared to b and c will be
compared to b. Treatment b will be used when
estimating the effect of other factors. Category variables can have
category labels listed (in quotes), or an unquoted number that is a
legal level, if all levels are numeric. You need only use the
first few letters of each variable name - enough for unique
identification. For variables not defined with datadist, you
must specify 3 values, none of which are NA.
Also represents other arguments to pass to latex, is ignored for
print and plot.
must be specified if the fit is a partial proportional odds model. Specifies the single value of the response variable used to estimate ycut-specific regression effects, e.g., odds ratios
Set to FALSE to only estimate effects of variables listed. Default is TRUE.
Set to FALSE to suppress printing of anti-logged effects. Default
is TRUE if the model was fitted by lrm or cph.
Antilogged effects will be odds ratios for logistic models and hazard ratios
for proportional hazards models.
Defaults to .95 for 95% confidence intervals of effects.
Set to TRUE to use the abbreviate function to shorten
factor levels for categorical variables in the model.
Set to "labels" to use variable labels to label effects.
Default is "names" to use variable names.
The default type of confidence interval computed for a given
individual (1 d.f.) contrast is a pointwise confidence interval. Set
conf.type="simultaneous" to use the multcomp package's
glht and confint functions to compute confidence
intervals with simultaneous (family-wise) coverage, thus adjusting for
multiple comparisons. Contrasts are simultaneous only over groups of
intervals computed together.
If fit was the result of bootcov but you want to use the
bootstrap covariance matrix instead of the nonparametric percentile,
basic, or BCa methods for confidence intervals (which uses all the bootstrap
coefficients), specify usebootcoef=FALSE.
set to 'bca' to compute BCa confidence
limits or to 'basic' to use the basic bootstrap. The default
is to compute percentile intervals.
set to 'mode' or 'median' to use the posterior
mean/median instead of the mean for point estimates of contrasts
set to TRUE when conf.type='simultaneous'
to get output describing scope of simultaneous adjustments
result of summary
title to pass to latex. Default is name of fit object passed to
summary prefixed with "summary".
see latex
for html.summary.rms; digits is the
number of significant digits for printing for effects, standard
errors, and confidence limits. It is ignored if dec is
given. The statistics are rounded to dec digits to the right of
the decimal point of dec is given. digits is also the
number of significant digits to format numeric hover text and labels
for plotly.
number of digits to the right of the decimal point to which to round confidence limits for labeling axes
vector of coordinates at which to put tick mark labels on the main axis. If
log=TRUE, at should be in anti-log units.
Set to TRUE to plot on
scalar or vector of confidence coefficients to depict
X-axis limits for plot in units of the linear predictors (log scale
if log=TRUE). If at is specified and xlim is
omitted, xlim is derived from the range of at.
Sets up plot to leave room for nbar horizontal bars. Default is the
number of non-interaction factors in the model. Set nbar to a larger
value to keep too much surrounding space from appearing around horizontal
bars. If nbar is smaller than the number of bars, the plot is divided
into multiple pages with up to nbar bars on each page.
cex parameter for factor labels.
Number of tick mark numbers for pretty.
cex parameter for main title. Set to 0 to
suppress the title.
confidence limits outside the interval c(clip[1], clip[2]) will be
ignored, and clip also be respected when computing xlim
when xlim is not specified. clip should be in the units of
fun(x). If log=TRUE, clip should be in
main title. Default is inferred from the model and value of log,
e.g., "log Odds Ratio".
vector of colors, one per value of q
color for points estimates
symbol for point estimates. Default is solid triangle.
line width for confidence intervals, corresponding to
q
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*(age-50) +
(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
if (FALSE) {
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
}
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 non-categorical
#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)
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