Description Usage Arguments Details Value References Examples

Point-Biserial correlation coefficient is a correlation coefficient used when one variable is continuous and the other variable is dichotomous. Taken from ltm::biserial_cor

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

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

`y` |
a numeric vector representing the dichotomous variable. |

`use` |
is a option for the use of missing values. |

`level` |
which level of y to use. |

It is calculated by applying the Pearson correlation coefficient to the case
where one of the variables has dichotomous nature.

It is calculated as

*r_{xy} = (\bar{x}_p - \bar{x}_q / S_x)*√{pq}*

Where p is the proportion of subjects with one of the two possible values of the variable Y, q is the proportion of subjects with the other possible value,

*\bar{x}_p*

and

*\bar{x}_q*

is the average X subjects whose proportion is p and q respectively, and

*S_x*

is the standard deviation of all subjects X. This function was adapted from ltm_1.0 package.

The value of the point-biserial correlation.

U.Olsson, F.Drasgow, and N.Dorans (1982). The polyserial correlation coefficient. Psychometrika, 47:337-347.

Cox. N.R. (1974). Estimation of the Correlation between a Continuous and a Discrete Variable. Biometrics, 30:171-178.

1 2 3 4 5 | ```
## Not run:
data <- simulate_dichotomous(size.cluster = c(10),sample.size=1000)
biserial_cor(rowSums(data$data), data$data[,1])
## End(Not run)
``` |

LatentREGpp documentation built on April 14, 2017, 11:55 a.m.

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