The main use is to take a fitted logbin.smooth
object produced by
logbin.smooth
and plot the component smooth functions that make it up,
for specified values of the other covariates.
Alternatively, plots the model diagnostics usually provided by plot.lm
.
1 2 3 
x 
a fitted 
type 
for for 
at 
a data frame containing the values at which the prediction should be evaluated. The columns
must contain the covariates in the model, and several rows may be provided (in which case, multiple
lines are drawn on the same plot). Cannot be missing or 
knotlines 
logical; if vertical lines should be drawn on the plot to indicate the locations of the knots for Bspline terms. 
nobs 
the number of points which should be used to create the curve. These are placed evenly along the range of the observed covariate values from the original model. 
... 
other graphics parameters to pass on to plotting commands, in particular any arguments to

For each smooth covariate in the model of x
, predict.logbin.smooth
is used to obtain predicted values for the range of that covariate, with the other
covariates remaining fixed at their values given in at
. Several rows may be provided
in at
, in which case, one curve is drawn for each, and they are coloured using
rainbow(nrow(at))
. If the model contains a single smooth covariate and no other
covariates, at
may be provided as an empty data frame, data.frame()
.
The function simply generates plots.
If this function is too restrictive, it may be easier to use predict.logbin.smooth
to get predictions for the dataset of your choice, and do the plotting manually.
Mark W. Donoghoe markdonoghoe@gmail.com
logbin.smooth
, predict.logbin.smooth
1  ## For an example, see example(logbin.smooth)

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