R: A wrapper for 'termplot' that optionally (but by default)...

Termplot {Epi} | R Documentation

## A wrapper for `termplot` that optionally (but by default) exponentiates terms, and plot them on a common log-scale. Also scales x-axes to the same physical scale.

### Description

The function uses `termplot` to extract terms from a model with, say, spline, terms, including the standard errors, computes confidence intervals and transform these to the rate / rate-ratio scale. Thus the default use is for models on the log-scale such as Poisson-regression models. The function produces a plot with panels side-by-side, one panel per term, and returns the

### Usage

      Termplot( obj,
               plot = TRUE,
               xlab = NULL,
               ylab = NULL,
                xeq = TRUE,
               yshr = 1,
              alpha = 0.05,
              terms = NULL,
             max.pt = NULL )

### Arguments

`obj` | An object with a `terms`-method — for details the the documentation for `termplot`.
`plot` | Should a plot be produced?
`xlab` | Labels for the `x`-axes. Defaults to the names of the terms.
`ylab` | Labels for the `x`-axes. Defaults to blank.
`xeq` | Should the units all all plots have the same physical scale for the `x`-axes).
`yshr` | Shrinking of `y`-axis. By default, the `y`-axes have an extent that accommodates the entire range of confidence intervals. This is a shrinking parameter for the `y`-axes, setting it to less than 1 will lose a bit of the confidence limits on some of the panels.
`alpha` | 1 minus the confidence level for computing confidence intervals
`terms` | Which terms should be reported. Passed on to `termplot` and eventually `predict`.
`max.pt` | The maximal number of points in which to report the terms. If `NULL` all unique points from the analysis dataset are reported for each term (this is a feature of `termplot`).

### Value

A list with one component per term in the model object `obj`, each component is a 4-column matrix with $x$ as the first column, and 3 columns with estimae and lower and upper confidence limit.

### Author(s)

Bendix Cartensen

### See Also

`Ns`, `termplot`

### Examples

    # Get the diabetes data and set up as Lexis object
    data(DMlate)
    DMlate <- DMlate[sample(1:nrow(DMlate),500),]
    dml <- Lexis( entry = list(Per=dodm, Age=dodm-dobth, DMdur=0 ),
                   exit = list(Per=dox),
            exit.status = factor(!is.na(dodth),labels=c("DM","Dead")),
                   data = DMlate )

    # Split in 1-year age intervals
    dms <- splitLexis( dml, time.scale="Age", breaks=0:100 )

    # Model with 6 knots for both age and period
    n.kn <- 6
    # Model age-specific rates with period referenced to 2004
    ( a.kn <- with( subset(dms,lex.Xst=="Dead"),
                    quantile( Age+lex.dur, probs=(1:n.kn-0.5)/n.kn ) ) )
    ( p.kn <- with( subset(dms,lex.Xst=="Dead"),
                    quantile( Per+lex.dur, probs=(1:n.kn-0.5)/n.kn ) ) )
    m2 <- glm( lex.Xst=="Dead" ~ -1 +
                                 Ns( Age, kn=a.kn, intercept=TRUE ) +
                                 Ns( Per, kn=p.kn, ref=2004 ),
               offset = log( lex.dur ), family=poisson, data=dms )

    # Finally we can plot the two effects:
    Termplot( m2, yshr=0.9 )

[Package Epi version 2.37 Index]