predict.cgaim: 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 cgaim
predict(object, newdata, type = c("response", "terms",
  "scterms", "indices"), select = NULL, na.action = "na.pass", ...)

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.

Arguments

object: A gaim object.
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. type = "response" returns the predicted response. type = "terms", returns ridge and smooth functions evaluated at index predicted for newdata. type = "scterms" is the same, except that terms are postmultiplied by their scaling coefficients beta. type = "indices" returns predicted indices values.
select: A numeric or character vector indicating terms to return for all types except "response".
na.action: A function indicating how to treat NAs. See na.fail.
...: For compatibility with the default predict method. Unused at the moment.

Details

Predictions of ridge functions at new index values is made through natural interpolation splines, which ensures shape-constraints are enforced. When predicted index result in values outside the observed range, natural splines ensure linear extrapolation, which respects any shape-constraint.

type = "terms" returns the scaled ridge functions, i.e. before being multiplied by scaling coefficients beta.

Examples

Run this code


## 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)