OPCreg (version 2.0.0)

SPCR: The stochastic principal component method can handle online data sets.

Description

The stochastic principal component method can handle online data sets.

Usage

SPCR(data, eta, m)

Value

A list containing the following elements:

Bhat

The estimated regression coefficients, including the intercept.

RMSE

The Root Mean Square Error of the regression model.

summary

The summary of the linear regression model.

yhat

The predicted values of the response variable.

Arguments

data

A data frame containing the response variable and predictors.

eta

proportion (between 0 and 1) determining the initial sample size for PCA.

m

The number of principal components to retain.

Examples

# Example data
library(MASS);library(stats)
set.seed(1234)
n <- 2000
p <- 10
mu0 <- as.matrix(runif(p, 0))
sigma0 <- as.matrix(runif(p, 0, 10))
ro <- as.matrix(c(runif(round(p / 2), -1, -0.8), runif(p - round(p / 2), 0.8, 1)))
R0 <- ro %*% t(ro)
diag(R0) <- 1
Sigma0 <- sigma0 %*% t(sigma0) * R0
x <- mvrnorm(n, mu0, Sigma0)
colnames(x) <- paste("x", 1:p, sep = "")
e <- rnorm(n, 0, 1)
B <- sample(1:3, (p + 1), replace = TRUE)
en <- matrix(rep(1, n * 1), ncol = 1)
y <- cbind(en, x) %*% B + e
colnames(y) <- paste("y")
data <- data.frame(cbind(y, x))
result <- SPCR(data,  eta = 0.0035, m = 3)