vgam-class object can
be plotted with plotvgam(). These are on the scale of
the linear/additive predictor.plotvgam(x, newdata = NULL, y = NULL, residuals = NULL, rugplot = TRUE, se = FALSE, scale = 0, raw = TRUE, offset.arg = 0, deriv.arg = 0, overlay = FALSE, type.residuals = c("deviance", "working", "pearson", "response"), plot.arg = TRUE, which.term = NULL, which.cf = NULL, control = plotvgam.control(...), varxij = 1, ...)
TRUE then residuals are plotted.
See type.residuals
TRUE then a rug plot is plotted at the
foot of each plot. These values are jittered to expose ties.
TRUE then approximate $+-2$ pointwise
standard error bands are included in the plot.
scale wide.
TRUE then the smooth functions are those
obtained directly by the algorithm, and are plotted without
having to premultiply with the constraint matrices.
If FALSE then the smooth functions have been premultiply by
the constraint matrices.
The raw argument is directly fed into predict.vgam().
overlay is TRUE.
If overlay is TRUE and there is one covariate then
using the intercept values as the offsets can be a good idea.
s() terms,
it plots the derivative.
TRUE then component functions of the same
covariate are overlaid on each other.
The functions are centered, so offset.arg can be useful
when overlay is TRUE.
residuals is TRUE then the first
possible value
of this vector, is used to specify the type of residual.
FALSE then no plot is produced.
which.term = c("s(age)", "s(height")) or
which.term = c(2, 5, 9).
By default, all are plotted.
plotvgam.control.
plotvgam.control. This includes line colors,
line widths, line types, etc.
xij of vglm.control was used,
this chooses which inner argument the component is plotted against.
This argument is related to raw = TRUE and terms such as
NS(dum1, dum2) and constraint matrices that have more than
one column. The default would plot the smooth against dum1
but setting varxij = 2 could mean plotting the smooth against
dum2.
See the VGAM website for further information.
preplot slot of the object
assigned information regarding the plot.
Many of plotvgam()'s options can be found in
plotvgam.control, e.g., line types, line widths,
colors.
vgam,
plotvgam.control,
predict.vgam,
plotvglm,
vglm.
coalminers <- transform(coalminers, Age = (age - 42) / 5)
fit <- vgam(cbind(nBnW, nBW, BnW, BW) ~ s(Age),
binom2.or(zero = NULL), data = coalminers)
## Not run: par(mfrow = c(1,3))
# plot(fit, se = TRUE, ylim = c(-3, 2), las = 1)
# plot(fit, se = TRUE, which.cf = 1:2, lcol = "blue", scol = "orange",
# ylim = c(-3, 2))
# plot(fit, se = TRUE, which.cf = 1:2, lcol = "blue", scol = "orange",
# overlay = TRUE) ## End(Not run)
Run the code above in your browser using DataLab