Plot a fractional polynomial curve estimate using samples from a single GLM / Cox model or a model average.

```
plotCurveEstimate(
samples,
termName,
plevel = 0.95,
slevel = plevel,
plot = TRUE,
rug = FALSE,
addZeros = FALSE,
...
)
```

termName

string denoting an FP term, as written by the
`as.data.frame`

method

plevel

credible level for pointwise HPD, and `NULL`

means
no pointwise HPD (default: 0.95). The pointwise intervals are plotted in
blue color.

slevel

credible level for simultaneous credible band (SCB),
`NULL`

means no SCB (defaults to `plevel`

). The simultaneous
intervals are plotted in green color.

plot

if `FALSE`

, only return values needed to produce the
plot, but do not plot (default is `TRUE`

, so a plot is made)

rug

add a rug to the plot? (default: `FALSE`

)

addZeros

include zero samples for models where the covariate is not
included? (default: `FALSE`

) If `TRUE`

, this changes the
interpretation of the samples, and therefore curve estimates based on these
samples: it is no longer conditional on inclusion of the covariate, but
marginally over all models, also those not including the covariate.

…

further arguments for plotting with `matplot`

a list of various plotting information:

grid on the original covariate scale

grid on the transformed scale

pointwise mean curve values

lower boundaries for pointwise HPD

upper boundaries for pointwise HPD

lower boundaries for SCB

upper boundaries for SCB

observed values of the covariate on the original scale

not implemented: partial residuals

vector of shift and scale parameter