This function performs sample size computation for testing Pearson's correlation coefficient based on precision requirements (i.e., type-I-risk, type-II-risk and an effect size).

1 2 3 |

`rho` |
a number indicating the correlation coefficient under the null hypothesis, |

`delta` |
minimum difference to be detected, |

`alternative` |
a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". |

`alpha` |
type-I-risk, |

`beta` |
type-II-risk, |

`output` |
logical: if |

Returns an object of class `size`

with following entries:

`call` | function call |

`type` | type of the test (i.e., correlation coefficient) |

`spec` | specification of function arguments |

`res` | list with the result, i.e., optimal sample size |

Takuya Yanagida takuya.yanagida@univie.ac.at,

Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). *Statistics in psychology - Using R and SPSS*.
New York: John Wiley & Sons.

Rasch, D., Pilz, J., Verdooren, L. R., & Gebhardt, G. (2011).
*Optimal experimental design with R*. Boca Raton: Chapman & Hall/CRC.

`seqtest.cor`

, `size.mean`

, `size.prop`

, `print.size`

1 2 3 4 5 6 7 8 9 10 11 | ```
#--------------------------------------
# H0: rho = 0.3, H1: rho != 0.3
# alpha = 0.05, beta = 0.2, delta = 0.2
size.cor(rho = 0.3, delta = 0.2, alpha = 0.05, beta = 0.2)
#--------------------------------------
# H0: rho <= 0.3, H1: rho > 0.3
# alpha = 0.05, beta = 0.2, delta = 0.2
size.cor(rho = 0.3, delta = 0.2, alternative = "greater", alpha = 0.05, beta = 0.2)
``` |

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