R: Simulated Linear Regression (Train) with Nine Highly...

hivif {bestglm} | R Documentation

## 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)

[Package bestglm version 0.36 Index]