rmvm {asbio} R Documentation

## A multivariate normal dataset for data mining

### Description

Contains a Y variable constrained to be a random function of fifteen X variables, which, in turn, are generated from a multivariate normal distribution with no correlation between dimensions.

### Usage

`data("rmvm")`

### Format

A data frame with 500 observations on the following 16 variables.

`Y`

A response vector defined to be: Y = X_1 + X_2 + X_3 + X_4 + X_5 + X_6 + X_7 + X_8 + X_9 + X_{10} + X_{11} + X_{12} + X_{13} + X_{14} + X_{15} + ε where ε \sim N(0, 1).

`X1`

A random predictor

`X2`

A random predictor

`X3`

A random predictor

`X4`

A random predictor

`X5`

A random predictor

`X6`

A random predictor

`X7`

A random predictor

`X8`

A random predictor

`X9`

A random predictor

`X10`

A random predictor

`X11`

A random predictor

`X12`

A random predictor

`X13`

A random predictor

`X14`

A random predictor

`X15`

A random predictor

### Details

Data used by Derryberry et al. (in review) to consider high dimensional model selection applications.

### References

Derryberry, D., Aho, K., Peterson, T., Edwards, J. (In review). Finding the "best" second order regression model in a polynomial number of steps. American Statistician.

### Examples

```## Code used to create data
## Not run:
sigma <- matrix(nrow = 15, ncol = 15, 0)
diag(sigma) = 1
mvn <- rmvnorm(n=500, mean=rnorm(15), sigma=sigma)
Y <- mvn[,1] + mvn[,2] + mvn[,3] + mvn[,4] + mvn[,4] + mvn[,5] + mvn[,6] + mvn[,7] +
mvn[,8] + mvn[,9] + mvn[,10] + mvn[,11] + mvn[,12] + mvn[,13] + mvn[,14] + mvn + rnorm(500)
rmvm <- data.frame(cbind(Y, mvn))
names(rmvm) <- c("Y", paste("X", 1:15, sep = ""))

## End(Not run)
```

[Package asbio version 1.7 Index]