Default VGAM Plotting

Component functions of a vgam-class object can be plotted with plotvgam(). These are on the scale of the linear/additive predictor.

Usage

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, ...)

Arguments

x

A fitted VGAM object, e.g., produced by vgam, vglm, or rrvglm.

newdata

Data frame. May be used to reconstruct the original data set.

y

Unused.

residuals

Logical. If TRUE then residuals are plotted. See type.residuals

rugplot

Logical. If TRUE then a rug plot is plotted at the foot of each plot. These values are jittered to expose ties.

se

Logical. If TRUE then approximate \(\pm 2\) pointwise standard error bands are included in the plot.

scale

Numerical. By default, each plot will have its own y-axis scale. However, by specifying a value, each plot's y-axis scale will be at least scale wide.

raw

Logical. If 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().

offset.arg

Numerical vector of length \(r\). These are added to the component functions. Useful for separating out the functions when 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.

deriv.arg

Numerical. The order of the derivative. Should be assigned an small integer such as 0, 1, 2. Only applying to s() terms, it plots the derivative.

overlay

Logical. If 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.

type.residuals

if residuals is TRUE then the first possible value of this vector, is used to specify the type of residual.

plot.arg

Logical. If FALSE then no plot is produced.

which.term

Character or integer vector containing all terms to be plotted, e.g., which.term = c("s(age)", "s(height")) or which.term = c(2, 5, 9). By default, all are plotted.

which.cf

An integer-valued vector specifying which linear/additive predictors are to be plotted. The values must be from the set {1,2,...,\(r\)}. By default, all are plotted.

control

Other control parameters. See plotvgam.control.

...

Other arguments that can be fed into plotvgam.control. This includes line colors, line widths, line types, etc.

varxij

Positive integer. Used if 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.

Details

In this help file \(M\) is the number of linear/additive predictors, and \(r\) is the number of columns of the constraint matrix of interest.

Many of plotvgam()'s options can be found in plotvgam.control, e.g., line types, line widths, colors.

Value

The original object, but with the preplot slot of the object assigned information regarding the plot.

Author

Thomas W. Yee

Note

While plot(fit) will work if class(fit) is "vgam", it is necessary to use plotvgam(fit) explicitly otherwise.

plotvgam() is quite buggy at the moment.

Examples

coalminers <- transform(coalminers, Age = (age - 42) / 5)
fit <- vgam(cbind(nBnW, nBW, BnW, BW) ~ s(Age),
            binom2.or(zero = NULL), data = coalminers)
if (FALSE)  par(mfrow = c(1,3))
plot(fit, se = TRUE, ylim = c(-3, 2), las = 1)
#> Error in eval(predvars, data, env): object 'Age' not found
plot(fit, se = TRUE, which.cf = 1:2, lcol = "blue", scol = "orange",
     ylim = c(-3, 2))
#> Error in eval(predvars, data, env): object 'Age' not found
plot(fit, se = TRUE, which.cf = 1:2, lcol = "blue", scol = "orange",
     overlay = TRUE)  # \dontrun{}
#> Error in eval(predvars, data, env): object 'Age' not found