rms (version 5.1-2)

anova.rms: Analysis of Variance (Wald and F Statistics)


The anova function automatically tests most meaningful hypotheses in a design. For example, suppose that age and cholesterol are predictors, and that a general interaction is modeled using a restricted spline surface. anova prints Wald statistics (\(F\) statistics for an ols fit) for testing linearity of age, linearity of cholesterol, age effect (age + age by cholesterol interaction), cholesterol effect (cholesterol + age by cholesterol interaction), linearity of the age by cholesterol interaction (i.e., adequacy of the simple age * cholesterol 1 d.f. product), linearity of the interaction in age alone, and linearity of the interaction in cholesterol alone. Joint tests of all interaction terms in the model and all nonlinear terms in the model are also performed. For any multiple d.f. effects for continuous variables that were not modeled through rcs, pol, lsp, etc., tests of linearity will be omitted. This applies to matrix predictors produced by e.g. poly or ns. print.anova.rms is the printing method. plot.anova.rms draws dot charts depicting the importance of variables in the model, as measured by Wald \(\chi^2\), \(\chi^2\) minus d.f., AIC, \(P\)-values, partial \(R^2\), \(R^2\) for the whole model after deleting the effects in question, or proportion of overall model \(R^2\) that is due to each predictor. latex.anova.rms is the latex method. It substitutes Greek/math symbols in column headings, uses boldface for TOTAL lines, and constructs a caption. Then it passes the result to latex.default for conversion to LaTeX.

The print method calls latex or html methods depending on options(prType=), and output is to the console. For latex a table environment is not used and an ordinary tabular is produced.

html.anova.rms just calls latex.anova.rms.


# S3 method for rms
anova(object, …, main.effect=FALSE, tol=1e-9, 
      test=c('F','Chisq'), india=TRUE, indnl=TRUE, ss=TRUE,

# S3 method for anova.rms print(x, which=c('none','subscripts','names','dots'), table.env=FALSE, …)

# S3 method for anova.rms plot(x, what=c("chisqminusdf","chisq","aic","P","partial R2","remaining R2", "proportion R2", "proportion chisq"), xlab=NULL, pch=16, rm.totals=TRUE, rm.ia=FALSE, rm.other=NULL, newnames, sort=c("descending","ascending","none"), margin=c('chisq','P'), pl=TRUE, trans=NULL, ntrans=40, height=NULL, width=NULL, …)

# S3 method for anova.rms latex(object, title, dec.chisq=2, dec.F=2, dec.ss=NA, dec.ms=NA, dec.P=4, table.env=TRUE, caption=NULL, …)

# S3 method for anova.rms html(object, …)



a rms fit object. object must allow vcov to return the variance-covariance matrix. For latex is the result of anova.

If omitted, all variables are tested, yielding tests for individual factors and for pooled effects. Specify a subset of the variables to obtain tests for only those factors, with a pooled Wald tests for the combined effects of all factors listed. Names may be abbreviated. For example, specify anova(fit,age,cholesterol) to get a Wald statistic for testing the joint importance of age, cholesterol, and any factor interacting with them.

Can be optional graphical parameters to send to dotchart2, or other parameters to send to latex.default. Ignored for print.

For html.anova.rms the arguments are passed to latex.anova.rms.


Set to TRUE to print the (usually meaningless) main effect tests even when the factor is involved in an interaction. The default is FALSE, to print only the effect of the main effect combined with all interactions involving that factor.


singularity criterion for use in matrix inversion


For an ols fit, set test="Chisq" to use Wald \(\chi^2\) tests rather than F-tests.


set to FALSE to exclude individual tests of interaction from the table


set to FALSE to exclude individual tests of nonlinearity from the table


For an ols fit, set ss=FALSE to suppress printing partial sums of squares, mean squares, and the Error SS and MS.


set to 'labels' to use variable labels rather than variable names in the output


for print,plot,text is the result of anova.


If which is not "none" (the default), print.anova.rms will add to the rightmost column of the output the list of parameters being tested by the hypothesis being tested in the current row. Specifying which="subscripts" causes the subscripts of the regression coefficients being tested to be printed (with a subscript of one for the first non-intercept term). which="names" prints the names of the terms being tested, and which="dots" prints dots for terms being tested and blanks for those just being adjusted for.


what type of statistic to plot. The default is the Wald \(\chi^2\) statistic for each factor (adding in the effect of higher-ordered factors containing that factor) minus its degrees of freedom. The R2 choices for what only apply to ols models.


x-axis label, default is constructed according to what. plotmath symbols are used for R, by default.


character for plotting dots in dot charts. Default is 16 (solid dot).


set to FALSE to keep total \(\chi^2\)s (overall, nonlinear, interaction totals) in the chart.


set to TRUE to omit any effect that has "*" in its name


a list of other predictor names to omit from the chart


a list of substitute predictor names to use, after omitting any.


default is to sort bars in descending order of the summary statistic


set to a vector of character strings to write text for selected statistics in the right margin of the dot chart. The character strings can be any combination of "chisq", "d.f.", "P", "partial R2", "proportion R2", and "proportion chisq". Default is to not draw any statistics in the margin. When plotly is in effect, margin values are instead displayed as hover text.


set to FALSE to suppress plotting. This is useful when you only wish to analyze the vector of statistics returned.


set to a function to apply that transformation to the statistics being plotted, and to truncate negative values at zero. A good choice is trans=sqrt.


n argument to pretty, specifying the number of values for which to place tick marks. This should be larger than usual because of nonlinear scaling, to provide a sufficient number of tick marks on the left (stretched) part of the chi-square scale.


height and width of plotly plots drawn using dotchartp, in pixels. Ignored for ordinary plots. Defaults to minimum of 400 and 100 + 25 times the number of test statistics displayed.


title to pass to latex, default is name of fit object passed to anova prefixed with "anova.". For Windows, the default is "ano" followed by the first 5 letters of the name of the fit object.


number of places to the right of the decimal place for typesetting \(\chi^2\) values (default is 2). Use zero for integer, NA for floating point.


digits to the right for \(F\) statistics (default is 2)


digits to the right for sums of squares (default is NA, indicating floating point)


digits to the right for mean squares (default is NA)


digits to the right for \(P\)-values


see latex


caption for table if table.env is TRUE. Default is constructed from the response variable.


anova.rms returns a matrix of class anova.rms containing factors as rows and \(\chi^2\), d.f., and \(P\)-values as columns (or d.f., partial \(SS, MS, F, P\)). An attribute vinfo provides list of variables involved in each row and the type of test done. plot.anova.rms invisibly returns the vector of quantities plotted. This vector has a names attribute describing the terms for which the statistics in the vector are calculated.

Side Effects

print prints, latex creates a file with a name of the form "title.tex" (see the title argument above).


If the statistics being plotted with plot.anova.rms are few in number and one of them is negative or zero, plot.anova.rms will quit because of an error in dotchart2.

See Also

rms, rmsMisc, lrtest, rms.trans, summary.rms, plot.Predict, ggplot.Predict, solvet, locator, dotchart2, latex, xYplot, anova.lm, contrast.rms, pantext


Run this code
n <- 1000    # define sample size
set.seed(17) # so can reproduce the results
treat <- factor(sample(c('a','b','c'), n,TRUE))
num.diseases <- sample(0:4, n,TRUE)
age <- rnorm(n, 50, 10)
cholesterol <- rnorm(n, 200, 25)
weight <- rnorm(n, 150, 20)
sex <- factor(sample(c('female','male'), n,TRUE))
label(age) <- 'Age'      # label is in Hmisc
label(num.diseases) <- 'Number of Comorbid Diseases'
label(cholesterol) <- 'Total Cholesterol'
label(weight) <- 'Weight, lbs.'
label(sex) <- 'Sex'
units(cholesterol) <- 'mg/dl'   # uses units.default in Hmisc

# Specify population model for log odds that Y=1
L <- .1*(num.diseases-2) + .045*(age-50) +
     (log(cholesterol - 10)-5.2)*(-2*(treat=='a') +
# Simulate binary y to have Prob(y=1) = 1/[1+exp(-L)]
y <- ifelse(runif(n) < plogis(L), 1, 0)

fit <- lrm(y ~ treat + scored(num.diseases) + rcs(age) +
               log(cholesterol+10) + treat:log(cholesterol+10))
a <- anova(fit)                       # Test all factors
b <- anova(fit, treat, cholesterol)   # Test these 2 by themselves
                                      # to get their pooled effects
# Add a new line to the plot with combined effects
s <- rbind(a, 'treat+cholesterol'=b['TOTAL',])
class(s) <- 'anova.rms'
plot(s, margin=c('chisq', 'proportion chisq'))

g <- lrm(y ~ treat*rcs(age))
dd <- datadist(treat, num.diseases, age, cholesterol)
p <- Predict(g, age, treat="b")
s <- anova(g)
# Usually omit fontfamily to default to 'Courier'
# It's specified here to make R pass its package-building checks
plot(p, addpanel=pantext(s, 28, 1.9,  fontfamily='Helvetica'))

plot(s, margin=c('chisq', 'proportion chisq'))
# new plot - dot chart of chisq-d.f. with 2 other stats in right margin
# latex(s)                       # nice printout - creates anova.g.tex

# Simulate data with from a given model, and display exactly which
# hypotheses are being tested

age <- rnorm(500, 50, 15)
treat <- factor(sample(c('a','b','c'), 500, TRUE))
bp  <- rnorm(500, 120, 10)
y   <- ifelse(treat=='a', (age-50)*.05, abs(age-50)*.08) + 3*(treat=='c') +
       pmax(bp, 100)*.09 + rnorm(500)
f   <- ols(y ~ treat*lsp(age,50) + rcs(bp,4))
print(names(coef(f)), quote=FALSE)
an <- anova(f)
print(an, 'subscripts')
print(an, 'dots')

an <- anova(f, test='Chisq', ss=FALSE)
plot(0:1)                        # make some plot
tab <- pantext(an, 1.2, .6, lattice=FALSE, fontfamily='Helvetica')
# create function to write table; usually omit fontfamily
tab()                            # execute it; could do tab(cex=.65)
plot(an)                         # new plot - dot chart of chisq-d.f.
# Specify plot(an, trans=sqrt) to use a square root scale for this plot
# latex(an)                      # nice printout - creates anova.f.tex

## Example to save partial R^2 for all predictors, along with overall
## R^2, from two separate fits, and to combine them with a lattice plot

n <- 100
x1 <- runif(n)
x2 <- runif(n)
y  <- (x1-.5)^2 + x2 + runif(n)
group <- c(rep('a', n/2), rep('b', n/2))
for(g in c('a','b')) {
    f <- ols(y ~ pol(x1,2) + pol(x2,2) + pol(x1,2) %ia% pol(x2,2),
    a <- plot(anova(f),
              what='partial R2', pl=FALSE, rm.totals=FALSE, sort='none')
    a <- a[-grep('NONLINEAR', names(a))]
    d <- data.frame(group=g, Variable=factor(names(a), names(a)),
    A <- rbind(A, d)
dotplot(Variable ~ partialR2 | group, data=A,
        xlab=ex <- expression(partial~R^2))
dotplot(group ~ partialR2 | Variable, data=A, xlab=ex)
dotplot(Variable ~ partialR2, groups=group, data=A, xlab=ex,

# Suppose that a researcher wants to make a big deal about a variable 
# because it has the highest adjusted chi-square.  We use the
# bootstrap to derive 0.95 confidence intervals for the ranks of all
# the effects in the model.  We use the plot method for anova, with
# pl=FALSE to suppress actual plotting of chi-square - d.f. for each
# bootstrap repetition.  
# It is important to tell plot.anova.rms not to sort the results, or
# every bootstrap replication would have ranks of 1,2,3,... for the stats.

n <- 300
d <- data.frame(x1=runif(n), x2=runif(n),  x3=runif(n),
   x4=runif(n), x5=runif(n), x6=runif(n),  x7=runif(n),
   x8=runif(n), x9=runif(n), x10=runif(n), x11=runif(n),
d$y <- with(d, 1*x1 + 2*x2 + 3*x3 +  4*x4  + 5*x5 + 6*x6 +
               7*x7 + 8*x8 + 9*x9 + 10*x10 + 11*x11 +
              12*x12 + 9*rnorm(n))

f <- ols(y ~ x1+x2+x3+x4+x5+x6+x7+x8+x9+x10+x11+x12, data=d)
B <- 20   # actually use B=1000
ranks <- matrix(NA, nrow=B, ncol=12)
rankvars <- function(fit)
  rank(plot(anova(fit), sort='none', pl=FALSE))
Rank <- rankvars(f)
for(i in 1:B) {
  j <- sample(1:n, n, TRUE)
  bootfit <- update(f, data=d, subset=j)
  ranks[i,] <- rankvars(bootfit)
lim <- t(apply(ranks, 2, quantile, probs=c(.025,.975)))
predictor <- factor(names(Rank), names(Rank))
Dotplot(predictor ~ Cbind(Rank, lim), pch=3, xlab='Rank') 
# }

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