hivif {bestglm}

Simulated Linear Regression (Train) with Nine Highly Correlated Inputs

Description

The script that generated this data is given below.

Usage

data("hivif")

Format

A data frame with 1000 observations on the following 10 variables.

x1

a numeric vector

x2

a numeric vector

x3

a numeric vector

x4

a numeric vector

x5

a numeric vector

x6

a numeric vector

x7

a numeric vector

x8

a numeric vector

x9

a numeric vector

y

a numeric vector

Examples

#Simple example
data(hivif)
lm(y ~ ., data=hivif)
#
#This example shows how the original data was simulated and
#how additional test data may be simulated.
## Not run: 
 set.seed(778851) #needed for original training data
 n <- 100
 p <- 9 #9 covariates plus intercept
 sig <- toeplitz(0.9^(0:(p-1)))
 X <- MASS::mvrnorm(n=n, rep(0, p), Sigma=sig)
 colnames(X) <- paste0("x", 1:p)
 b <- c(0,-0.3,0,0,-0.3,0,0,0.3,0.3) #
 names(b) <- paste0("x", 1:p)
 y <- 1 +  X
 Xy <- cbind(as.data.frame.matrix(X), y=y) #=hivif
#Test data
 nTe <- 10^3
 XTe <- MASS::mvrnorm(n=nTe, rep(0, p), Sigma=sig)
 colnames(XTe) <- paste0("x", 1:p)
  yTe <- 1 +  XTe
 XyTe <- cbind(as.data.frame.matrix(XTe), y=yTe) #test data
 ans <- lm(y ~ ., data=Xy) #fit training data
 mean((XyTe$y - predict(ans, newdata=XyTe))^2) #MSE on test data
 
## End(Not run)