rms (version 3.1-0)

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).


## 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, ...)


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
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 proportio
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.
result of summary
title to pass to latex. Default is name of fit object passed to summary prefixed with "summary".
see latex
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 $X\beta$ scale but labeled with anti-logs.
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
cex parameter for factor labels.
Number of tick mark numbers for pretty.
cex parameter for confbar, for quantile labels.
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 f
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.


logistic regression model

See Also

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)

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))