Description Usage Arguments Value See Also Examples

`ttest.pexplore`

computes (via simulation) the power of an experiment that will be analyzed using a t-test for a range of sample sizes.
Rather than taking a theoretical distribution, this function takes empirical data and bootstraps them to calculate the power.
For an equivalent function that does not rely on pilot data see ttestpower.

`ttest.pexplore`

computes (via simulation) the power of an experiment that will be analyzed using a t-test for a range of sample sizes.
Rather than taking a theoretical distribution, this function takes empirical data and bootstraps them to calculate the power.
For an equivalent function that does not rely on pilot data see ttestpower.

1 2 3 4 5 6 7 | ```
ttest.pexplore(x, y = NULL, lown, topn, r = 10000,
alternative = c("two.sided", "less", "greater"), mu = NULL,
alpha = 0.05, conf.level = 0.95, plotit = TRUE)
ttest.pexplore(x, y = NULL, lown, topn, r = 10000,
alternative = c("two.sided", "less", "greater"), mu = NULL,
alpha = 0.05, conf.level = 0.95, plotit = TRUE)
``` |

`lown` |
smallest sample size to explore. |

`topn` |
largest sample size to explore. |

`r` |
number of simulations to compute power. |

`alternative` |
type of alternative hypothesis in binomial test. Must be " |

`mu` |
mean value according to null hypothesis (default = |

`alpha` |
significance threshhold. |

`plotit` |
logical (default= |

`lown` |
smallest sample size to explore. |

`topn` |
largest sample size to explore. |

`plotit` |
logical (default= |

The probability of finding *p < α* with the experiment description.

The probability of finding *p < α* with the experiment description.

`ttest.pow`

, `ttest.ppow`

, `ttest.explore`

, and `ttest.pexplore`

.

1 2 3 4 5 6 | ```
ttest.pexplore(x=c(0, 5, 10), lown=16, topn=24) # Power for a one-sample t-test with n in 16-24. Pilot data consists of three data points.
ttest.pexplore(x=c(0, 5, 10), lown=16, topn=24,mu = -5) # Same as above, changing the avarege under the null to -5.
ttest.pexplore(x=c(0, 5, 10), lown=16, topn=24, y=c(9, 3, 2, 1)) # Power for a two-sample t-test with n=16-24 (per condition) using unbalanced pilot data.
ttest.pexplore(x=c(0, 5, 10), lown=16, topn=24) # Power for a one-sample t-test with n in 16-24. Pilot data consists of three data points.
ttest.pexplore(x=c(0, 5, 10), lown=16, topn=24,mu = -5) # Same as above, changing the avarege under the null to -5.
ttest.pexplore(x=c(0, 5, 10), lown=16, topn=24, y=c(9, 3, 2, 1)) # Power for a two-sample t-test with n=16-24 (per condition) using unbalanced pilot data.
``` |

julianje/mcpa documentation built on May 13, 2019, 6:14 p.m.

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