pred.response: Predict the response with the fitted high-dimensional...
In abundant: High-Dimensional Principal Fitted Components and Abundant Regression

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/pred.response.R

Let x\in R^p denote the values of the p predictors. This function computes \widehat E(Y|X=x) using equation (8.1) of Cook, Forzani, and Rothman (2012).

1	pred.response(fit, newx=NULL)

`fit`	The object returned by `fit.pfc()`.
`newx`	A matrix with N rows and p columns where each row is an instance of x described above. If this argument is unspecified, then the fitted values are returned, i.e, `newx=X`, where `X` was the predictor matrix used in the call to `fit.pfc()`.

See Cook, Forzani, and Rothman (2012) for more information.

A vector of response prediction with nrow(newx) entries.

Adam J. Rothman

Cook, R. D., Forzani, L., and Rothman, A. J. (2012). Estimating sufficient reductions of the predictors in abundant high-dimensional regressions. Annals of Statistics 40(1), 353-384.

fit.pfc

set.seed(1)
n=25
p=50
d=1
true.G = matrix(rnorm(p*d), nrow=p, ncol=d)
y=rnorm(n)
fy = y
E=matrix(rnorm(n*p), nrow=n, ncol=p) 
X=fy%*%t(true.G) + E
fit=fit.pfc(X=X, r=4, d=d, y=y, weight.type="diag")
fitted.values=pred.response(fit)
mean((y-fitted.values)^2)
plot(fitted.values, y)

n.new=100
y.new=rnorm(n.new)
fy.new=y.new
E.new=matrix(rnorm(n.new*p), nrow=n.new, ncol=p) 
X.new = fy.new%*%t(true.G) + E.new
mean((y.new - pred.response(fit, newx=X.new))^2)