R: Analysis of Variance (Wald and F Statistics)

anova.rms {rms} | R Documentation

## Analysis of Variance (Wald and F Statistics)

### Description

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-square, chi-square 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`.

### Usage

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

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

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

### Arguments

`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`.
`main.effect` | 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.
`tol` | singularity criterion for use in matrix inversion
`test` | For an `ols` fit, set `test="Chisq"` to use Wald χ^2 tests rather than F-tests.
`india` | set to `FALSE` to exclude individual tests of interaction from the table
`indnl` | set to `FALSE` to exclude individual tests of nonlinearity from the table
`ss` | For an `ols` fit, set `ss=FALSE` to suppress printing partial sums of squares, mean squares, and the Error SS and MS.
`vnames` | set to `'labels'` to use variable labels rather than variable names in the output
`x` | for `print,plot,text` is the result of `anova`.
`which` | 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` | what type of statistic to plot. The default is the Wald chi-square 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.
`xlab` | x-axis label, default is constructed according to `what`. `plotmath` symbols are used for R, by default.
`pch` | character for plotting dots in dot charts. Default is 16 (solid dot).
`rm.totals` | set to `FALSE` to keep total chi-squares (overall, nonlinear, interaction totals) in the chart.
`rm.ia` | set to `TRUE` to omit any effect that has `"*"` in its name
`rm.other` | a list of other predictor names to omit from the chart
`newnames` | a list of substitute predictor names to use, after omitting any.
`sort` | default is to sort bars in descending order of the summary statistic
`margin` | 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.
`pl` | set to `FALSE` to suppress plotting. This is useful when you only wish to analyze the vector of statistics returned.
`trans` | 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`.
`ntrans` | `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,width` | 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` | 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.
`dec.chisq` | number of places to the right of the decimal place for typesetting chi-square values (default is `2`). Use zero for integer, `NA` for floating point.
`dec.F` | digits to the right for F statistics (default is `2`)
`dec.ss` | digits to the right for sums of squares (default is `NA`, indicating floating point)
`dec.ms` | digits to the right for mean squares (default is `NA`)
`dec.P` | digits to the right for P-values
`table.env` | see `latex`
`caption` | caption for table if `table.env` is `TRUE`. Default is constructed from the response variable.

### Details

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`.

### Value

`anova.rms` returns a matrix of class `anova.rms` containing factors as rows and chi-square, 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).

### Author(s)

Frank Harrell
Department of Biostatistics, Vanderbilt University
f.harrell@vanderbilt.edu

### See Also

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

### Examples

    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') +
         3.5*(treat=='b')+2*(treat=='c'))
    # 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
    a
    b
    # 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)
    options(datadist='dd')
    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
    options(datadist=NULL)

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

    set.seed(123)
    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)
    specs(f)
    anova(f)
    an <- anova(f)
    options(digits=3)
    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

    require(lattice)
    set.seed(1)
    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))
    A <- NULL
    for(g in c('a','b')) {
        f <- ols(y ~ pol(x1,2) + pol(x2,2) + pol(x1,2) %ia% pol(x2,2),
                 subset=group==g)
        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)),
                        partialR2=unname(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,
            auto.key=list(corner=c(.5,.5)))

    # 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
    set.seed(1)
    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),
       x12=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')

[Package rms version 5.1-1 Index]