Description Usage Arguments Details Examples

View source: R/genCorrelatedData.R

The output is a data frame (x1, x2, y) with user-specified
correlation between x1 and x2. The y (output) variable is created
according to the equation

*
y = beta1 + beta2 * x1 + beta3 * x2 + beta4 * x1 * x2 + e.
*

The arguments determine the scales of the X matrix, the random error, and the slope coefficients.

1 2 | ```
genCorrelatedData(N = 100, means = c(50, 50), sds = c(10, 10),
rho = 0, stde = 1, beta = c(0, 0.2, 0.2, 0))
``` |

`N` |
Number of cases desired |

`means` |
2-vector of means for x1 and x2 |

`sds` |
2-vector of standard deviations for x1 and x2 |

`rho` |
Correlation coefficient for x1 and x2 |

`stde` |
standard deviation of the error term in the data generating equation |

`beta` |
beta vector of at most 4 coefficients for intercept, slopes, and interaction |

The vector (x1,x2) is drawn from a multivariate normal
distribution in which the expected value (argument `means`

).
The covariance matrix of X is
built from the standard deviations (`sds`

)
and the specified correlation between x1 and x2 (`rho`

).
It is also necessary to specify the standard deviation
of the error term (`stde`

) and the coefficients
of the regression equation (`beta`

).

1 2 3 4 5 6 7 | ```
## 1000 observations of uncorrelated x1 and x2 with no
## interaction between x1 and x2
dat <- genCorrelatedData(N=1000, rho=0, beta=c(1, 1.0, -1.1, 0.0))
mcGraph1(dat$x1, dat$x2, dat$y, theta=20, phi=8,
ticktype="detailed", nticks=10)
m1 <- lm(y ~ x1 + x2, data = dat)
plotPlane(m1, plotx1 = "x1", plotx2 = "x2")
``` |

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