predict.cgaim {cgaim}  R Documentation 
Predictions from a fitted CGAIM object
Description
Uses a fitted cgaim
object and computes prediction for the
observed data or new data. Predicts the response, indices or
ridge functions values at the provided data.
Usage
## S3 method for class 'cgaim'
predict(object, newdata, type = c("response", "terms",
"scterms", "indices"), select = NULL, na.action = "na.pass", ...)
Arguments
object 
A 
newdata 
A list or data.frame containing the new data to predict. If missing, fitted values from the model are returned. 
type 
A character indicating the type of prediction to return.

select 
A numeric or character vector indicating terms to return
for all types except 
na.action 
A function indicating how to treat NAs. See

... 
For compatibility with the default 
Details
type = "terms"
returns the scaled ridge functions, i.e. before being multiplied by scaling coefficients beta.
Value
When type = "response"
returns a vector of predicted response.
When type = "terms"
or "scterms"
, returns a matrix of evaluated ridge and
smooth terms. When type = "indices"
, returns
a matrix of evaluated indices.
See Also
cgaim
for main fitting function
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 < mu + rnorm(n)
df1 < data.frame(y, x1, x2, x3, x4)
## Fit an unconstrained the model
ans < cgaim(y ~ g(x1, x2, label = "foo") + g(x3, x4, label = "bar"),
data = df1)
## Get fitted values
yhat < predict(ans)
## Predict on new data
newdf < as.data.frame(matrix(rnorm(100), 25, 4))
names(newdf) < sprintf("x%i", 1:4)
# predicted response
ypred < predict(ans, newdf)
# Indices
indices < predict(ans, newdata = newdf, type = "indices")
# Ridge functions
funs < predict(ans, newdata = newdf, type = "terms")
## Select specific terms
ind1 < predict(ans, newdata = newdf, select = "foo", type = "indices")
fun1 < predict(ans, newdata = newdf, select = "foo", type = "terms")
# Plot
plot(ans, select = "foo")
points(ind1, fun1)
## Scaled terms
fun2 < predict(ans, newdata = newdf, select = "foo", type = "scterms")
# Plot
plot(ans, select = "foo", yscale = TRUE)
points(ind1, fun2)