summary.rms: Summary of Effects in Model
In rms: Regression Modeling Strategies

summary.rms

R Documentation

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 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.

Usage

## S3 method for class '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 class 'summary.rms'
print(x, ..., table.env=FALSE)

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

## S3 method for class 'summary.rms'
html(object, digits=4, dec=NULL, ...)

## 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=if(length(q) == 1) 3 else 2 : (length(q) + 1), digits=4,
    declim=4, ...)

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 `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`.
`ycut`	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
`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 proportional hazards models.
`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` 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.
`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), specify `usebootcoef=FALSE`.
`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.
`posterior.summary`	set to `'mode'` or `'median'` to use the posterior mean/median instead of the mean for point estimates of contrasts
`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`
`digits`, `dec`	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`.
`declim`	number of digits to the right of the decimal point to which to round confidence limits for labeling axes
`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 `nbar` is smaller than the number of bars, the plot is divided into multiple pages with up to `nbar` bars on each page.
`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 `fun(x)`. If `log=TRUE`, `clip` should be in `X\beta` units.
`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, corresponding to `q`

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.

Author(s)

Frank Harrell
Hui Nian
Department of Biostatistics, Vanderbilt University
fh@fharrell.com

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
## Not run: 
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

## End(Not run)
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)

rms documentation built on Sept. 11, 2024, 7:51 p.m.