summary.rms: Summary of Effects in Model

Description

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: .9, .95, and .99, using blue of different transparencies.

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.

Usage

## S3 method for class 'rms':
summary(object, \dots, 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"), verbose=FALSE)
## 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(0.9, 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=3,
...)

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 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 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.

conf.type

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

usebootcoef

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), speci

boot.type

set to 'bca' to compute BCa confidence
	limits or to 'basic' to use the basic bootstrap.  The default
	is to compute percentile intervals.

verbose

set to TRUE when conf.type='simultaneous'
  to get output describing scope of simultaneous adjustments

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.main

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".

col

vector of colors, one per value of q

col.points

color for points estimates

pch

symbol for point estimates. Default is solid triangle.

lwd

line width for confidence intervals

`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, 
Misc, pretty, contrast.rms

`Examples`

Run this coden <- 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)
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