draw.tetra | R Documentation |

A graphic of a correlation ellipse divided into 4 regions based upon x and y cutpoints on two normal distributions. This is also an example of using the layout function. Draw a bivariate density plot to show how tetrachorics work.

draw.tetra(r, t1, t2,shade=TRUE) draw.cor(r=.5,expand=10,theta=30,phi=30,N=101,nbcol=30,box=TRUE, main="Bivariate density rho = ",cuts=NULL,all=TRUE,ellipses=TRUE,ze=.15)

`r` |
the underlying Pearson correlation defines the shape of the ellipse |

`t1` |
X is cut at tau |

`t2` |
Y is cut at Tau |

`shade` |
shade the diagram (default is TRUE) |

`expand` |
The relative height of the z axis |

`theta` |
The angle to rotate the x-y plane |

`phi` |
The angle above the plane to view the graph |

`N` |
The grid resolution |

`nbcol` |
The color resolution |

`box` |
Draw the axes |

`main` |
The main title |

`cuts` |
Should the graphic show cuts (e.g., cuts=c(0,0)) |

`all` |
Show all four parts of the tetrachoric |

`ellipses` |
Draw a correlation ellipse |

`ze` |
height of the ellipse if requested |

A graphic demonstration of the `tetrachoric`

correlation. Used for teaching purposes. The default values are for a correlation of .5 with cuts at 1 and 1. Any other values are possible. The code is also a demonstration of how to use the `layout`

function for complex graphics using base graphics.

William Revelle

`tetrachoric`

to find tetrachoric correlations, `irt.fa`

and `fa.poly`

to use them in factor analyses, `scatter.hist`

to show correlations and histograms.

#if(require(mvtnorm)) { #draw.tetra(.5,1,1) #draw.tetra(.8,2,1)} else {print("draw.tetra requires the mvtnorm package") #draw.cor(.5,cuts=c(0,0))} draw.tetra(.5,1,1) draw.tetra(.8,2,1) draw.cor(.5,cuts=c(0,0))

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