msir (version 1.3.3)

predict.msir: Model-based Sliced Inverse Regression directions

Description

MSIR estimates a set of \(d \le p\) orthogonal direction vectors of length \(p\) which are estimates of the basis of the dimensional reduction subspace.

Usage

# S3 method for msir
predict(object, dim = 1:object$numdir, newdata, …)

Arguments

object

an object of class 'msir' resulting from a call to msir.

dim

the dimensions of the reduced subspace used for prediction.

newdata

a data frame or matrix giving the data. If missing the data obtained from the call to msir are used.

…

further arguments passed to or from other methods.

Value

The function returns a matrix of points projected on the subspace spanned by the estimated basis vectors.

References

Scrucca, L. (2011) Model-based SIR for dimension reduction. Computational Statistics & Data Analysis, 55(11), 3010-3026.

See Also

{msir}

Examples

# NOT RUN {
n <- 200
p <- 5
b <- as.matrix(c(1,-1,rep(0,p-2)))
x <- matrix(rnorm(n*p), nrow = n, ncol = p)
y <- exp(0.5 * x%*%b) + 0.1*rnorm(n)
pairs(cbind(y,x), gap = 0)

MSIR <- msir(x, y)
summary(MSIR)
plot(MSIR, which = 1, type = "2Dplot")
all.equal(predict(MSIR), MSIR$dir)
predict(MSIR, dim = 1:2)

x0 <- matrix(rnorm(n*p), nrow = n, ncol = p)
y0 <- exp(0.5 * x0%*%b) + 0.1*rnorm(n)
plot(predict(MSIR, dim = 1, newdata = x0), y0)
# }