`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`

typesets
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: .7, .8, .9, .95, and .99, using
levels of gray scale (colors for Windows).```
## S3 method for class 'rms':
summary(object, \dots, est.all=TRUE, antilog,
conf.int=.95, abbrev=FALSE, vnames=c("names","labels"))
```## S3 method for class 'summary.rms':
print(x, \dots)

## S3 method for class 'summary.rms':
latex(object, title, table.env=TRUE, \dots)

## S3 method for class 'summary.rms':
plot(x, at, log=FALSE,
q=c(0.7, 0.8, 0.9, 0.95, 0.99), xlim, nbar, cex=1, nint=10,
cex.c=.5, cex.t=1, clip=c(-1e30,1e30), main, ...)

object

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 `summar`

...

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
est.all

Set to

`FALSE`

to only estimate effects of variables listed. Default is `TRUE`

.antilog

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 proportioconf.int

Defaults to

`.95`

for `95%`

confidence intervals of effects.abbrev

Set to

`TRUE`

to use the `abbreviate`

function to shorten
factor levels for categorical variables in the model.vnames

Set to

`"labels"`

to use variable labels to label effects.
Default is `"names"`

to use variable names.x

result of

`summary`

title

`title`

to pass to `latex`

. Default is name of fit object passed to
`summary`

prefixed with `"summary"`

.table.env

see

`latex`

at

vector of coordinates at which to put tick mark labels on the main axis. If

`log=TRUE`

, `at`

should be in anti-log units.log

Set to

`TRUE`

to plot on $X\beta$ scale but labeled with
anti-logs.q

scalar or vector of confidence coefficients to depict

xlim

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`

.nbar

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

cex

`cex`

parameter for factor labels.nint

Number of tick mark numbers for

`pretty`

.cex.c

`cex`

parameter for `confbar`

, for quantile labels.cex.t

`cex`

parameter for main title. Set to `0`

to suppress the title.clip

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
`f`

main

main title. Default is inferred from the model and value of

`log`

,
e.g., `"log Odds Ratio"`

.- 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.

`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 latex(s) # Use LaTeX to print nice version latex(s, file="") # Just write LaTeX code to screen 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)