# summary.rms

##### Summary of Effects in Model

`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).

##### Usage

```
## 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, \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, ...)

##### Arguments

- 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 test - 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 proportio - conf.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"`

.- 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"`

.

##### Value

- 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.

##### concept

logistic regression model

##### See Also

`datadist`

, `rms`

, `rms.trans`

, `rmsMisc`

,
`confbar`

, `pretty`

, `contrast.rms`

##### Examples

```
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)
```

*Documentation reproduced from package rms, version 2.0-2, License: GPL (>= 2)*