Description Usage Arguments Details Value Note Author(s) Examples

Computes the point-biserial correlation between a dichotomous and a continuous variable.

1 | ```
biserial.cor(x, y, use = c("all.obs", "complete.obs"), level = 1)
``` |

`x` |
a numeric vector representing the continuous variable. |

`y` |
a factor or a numeric vector (that will be converted to a factor) representing the dichotomous variable. |

`use` |
If |

`level` |
which level of |

The point biserial correlation computed by `biserial.cor()`

is defined as follows

*(X1.bar - X0.bar) * sqrt(pi * (1 - pi)) / S_x,*

where *X1.bar* and *X0.bar* denote the sample means of the *X*-values
corresponding to the first and second level of *Y*, respectively, *S_x* is the sample standard deviation of
*X*, and *pi* is the sample proportion for *Y = 1*. The first level of *Y* is defined by the
`level`

argument; see **Examples**.

the (numeric) value of the point-biserial correlation.

Changing the order of the levels for `y`

will produce a different result. By default, the first level is used
as a reference level

Dimitris Rizopoulos [email protected]

1 2 3 4 5 6 7 | ```
# the point-biserial correlation between
# the total score and the first item, using
# '0' as the reference level
biserial.cor(rowSums(LSAT), LSAT[[1]])
# and using '1' as the reference level
biserial.cor(rowSums(LSAT), LSAT[[1]], level = 2)
``` |

drizopoulos/ltm documentation built on April 19, 2018, 2:37 a.m.

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