Separately for each predictor variable \(X\) in a formula, plots the mean of \(X\) vs. levels of \(Y\). Then under the proportional odds assumption, the expected value of the predictor for each \(Y\) value is also plotted (as a dotted line). This plot is useful for assessing the ordinality assumption for \(Y\) separately for each \(X\), and for assessing the proportional odds assumption in a simple univariable way. If several predictors do not distinguish adjacent categories of \(Y\), those levels may need to be pooled. This display assumes that each predictor is linearly related to the log odds of each event in the proportional odds model. There is also an option to plot the expected means assuming a forward continuation ratio model.

```
# S3 method for xmean.ordinaly
plot(x, data, subset, na.action, subn=TRUE,
cr=FALSE, topcats=1, cex.points=.75, …)
```

x

an S formula. Response variable is treated as ordinal. For categorical predictors, a binary version of the variable is substituted, specifying whether or not the variable equals the modal category. Interactions or non-linear effects are not allowed.

data

a data frame or frame number

subset

vector of subscripts or logical vector describing subset of data to analyze

na.action

defaults to `na.keep`

so all NAs are initially retained. Then NAs
are deleted only for each predictor currently being plotted.
Specify `na.action=na.delete`

to remove observations that are missing
on any of the predictors (or the response).

subn

set to `FALSE`

to suppress a left bottom subtitle specifying the sample size
used in constructing each plot

cr

set to `TRUE`

to plot expected values by levels of the response,
assuming a forward continuation ratio model holds. The function is fairly slow
when this option is specified.

topcats

When a predictor is categorical, by default only the
proportion of observations in the overall most frequent category will
be plotted against response variable strata. Specify a higher value
of `topcats`

to make separate plots for the proportion in the
`k`

most frequent predictor categories, where `k`

is
`min(ncat-1, topcats)`

and `ncat`

is the number of unique
values of the predictor.

cex.points

if `cr`

is `TRUE`

, specifies the size of the
`"C"`

that is plotted. Default is 0.75.

...

other arguments passed to `plot`

and `lines`

plots

Harrell FE et al. (1998): Development of a clinical prediction model for an ordinal outcome. Stat in Med 17:909--44.

# NOT RUN { # Simulate data from a population proportional odds model set.seed(1) n <- 400 age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) region <- factor(sample(c('north','south','east','west'), n, replace=TRUE)) L <- .2*(age-50) + .1*(blood.pressure-120) p12 <- plogis(L) # Pr(Y>=1) p2 <- plogis(L-1) # Pr(Y=2) p <- cbind(1-p12, p12-p2, p2) # individual class probabilites # Cumulative probabilities: cp <- matrix(cumsum(t(p)) - rep(0:(n-1), rep(3,n)), byrow=TRUE, ncol=3) y <- (cp < runif(n)) %*% rep(1,3) # Thanks to Dave Krantz <dhk@paradox.psych.columbia.edu> for this trick par(mfrow=c(2,2)) plot.xmean.ordinaly(y ~ age + blood.pressure + region, cr=TRUE, topcats=2) par(mfrow=c(1,1)) # Note that for unimportant predictors we don't care very much about the # shapes of these plots. Use the Hmisc chiSquare function to compute # Pearson chi-square statistics to rank the variables by unadjusted # importance without assuming any ordering of the response: chiSquare(y ~ age + blood.pressure + region, g=3) chiSquare(y ~ age + blood.pressure + region, g=5) # }