plot.cgaim {cgaim}  R Documentation 
Plot ridge function
Description
Plot method for the ridge and smooth terms of a cgaim
object. If
provided, also plots confidence intervals.
Usage
## S3 method for class 'cgaim'
plot(x, select = NULL, ci = NULL, ci.plot = c("polygon",
"lines"), ci.args = list(), add = FALSE, xcenter = FALSE,
xscale = FALSE, yshift = FALSE, yscale = FALSE, ...)
Arguments
x 
A 
select 
A numeric or character vector indicating which terms to plot. 
ci 
An object returned by a call to 
ci.plot 
Whether to plot the confidence intervals as shaded areas

ci.args 
Additional arguments to be passed to the function used
to draw confidence interval. Either 
add 
Logical. If TRUE, adds the function to the current active plot. 
xcenter , xscale 
Centering and scaling values for the x axis. See

yshift , yscale 
Either logical or numeric values to shift and scale the ridge functions. See details. 
... 
Additional graphical parameters for the drawn function. See

Details
The values of yshift
and yscale
determine how
ridge functions are shifted and scaled for plotting. This can be used to
display the functions over data points for instance. If numeric, a vector
can be passed with one value for each plotted function. The vector is
recycled if necessary. This indicate the desired mean and standard deviation
of plotted ridge functions. Note that this is inverse to the parameters
in scale
(and xcenter,xscale
).
If TRUE is passed instead, functions are shifted
to the intercept and scaled to their corresponding beta coefficients, placing
them on the response scale.
Value
The function is called to generate plots and returns no value.
See Also
cgaim
for the main fitting function and
confint.cgaim
for confidence interval computation.
Examples
## Simulate some data
n < 200
x1 < rnorm(n)
x2 < rnorm(n)
x3 < rnorm(n)
x4 < rnorm(n)
mu < 4 * exp(8 * x1) / (1 + exp(8 * x1)) + exp(x3)
y < 5 + mu + rnorm(n)
df1 < data.frame(y, x1, x2, x3, x4)
## Fit a model
ans < cgaim(y ~ g(x1, x2, label = "foo") + g(x3, x4, label = "bar"),
data = df1)
## Default plot method
plot(ans)
## Select variable
plot(ans, select = 1)
# Same as
plot(ans, select = "foo")
## Add confidence intervals
ci < confint(ans)
plot(ans, select = 1, ci = ci)
## Change scale and location
# On the response scale
plot(ans, select = 1, ci = ci, yshift = TRUE, yscale = TRUE)
# Arbitrary scale
plot(ans, select = 1, ci = ci, yshift = 1000)
## Change look
# Main line
plot(ans, select = 1, ci = ci, col = 2, lwd = 3)
# Confidence intervals
plot(ans, select = 1, ci = ci, col = 2, lwd = 3,
ci.args = list(col = adjustcolor(2, .5)))
# Confidence interval type
plot(ans, select = 1, ci = ci, ci.plot = "lines", col = 2, lwd = 3,
ci.args = list(col = 2, lty = 4))
## Put curves on the same plot (need to shift and scale)
plot(ans, select = 1, col = 2, ylim = c(2, 3))
plot(ans, select = 2, col = 4, add = TRUE)