Description Usage Arguments Value See Also Examples

`ttest.explore`

computes (through simulation) the power of an experiment that will be analyzed using a t-test for a set of potential sample sizes. When two means are provided,
function assumes a two-sample unpaired t-test, and `n`

is interpreted as the sample size of each group (for a total sample size or `2n`

).

`ttest.explore`

computes (through simulation) the power of an experiment that will be analyzed using a t-test for a set of potential sample sizes. When two means are provided,
function assumes a two-sample unpaired t-test, and `n`

is interpreted as the sample size of each group (for a total sample size or `2n`

).

1 2 3 4 5 6 7 | ```
ttest.explore(lown, topn, means, var, r = 10000,
alternative = c("two.sided", "less", "greater"), mu = 0, alpha = 0.05,
conf.level = 0.95, plotit = TRUE)
ttest.explore(lown, topn, means, var, r = 10000,
alternative = c("two.sided", "less", "greater"), mu = 0, alpha = 0.05,
conf.level = 0.95, plotit = TRUE)
``` |

`lown` |
smallest sample size to explore. |

`topn` |
largest sample size to explore. |

`means` |
either a list with two average values (computes a two-sample t-test) or a single value (computes a one-sample t-test). |

`var` |
expected variance in each group. |

`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.

`ttest.pow`

, `ttest.ppow`

, `ttest.explore`

, and `ttest.pexplore`

.

1 2 3 4 5 6 | ```
ttest.explore(lown=10, topn=15, means=c(5, 10), var=10) # two-sample t-test. Effective sample sizes are 20 to 30 (10 to 15 per group)
ttest.explore(lown=10, topn=15, means=20, var=10) # one-sample t-test. Comparing if average is different from 0.
ttest.explore(lown=10, topn=15, means=20, var=10, mu=10, alternative="higher") # one-sample t-test. Comparing if average is higher than 10.
ttest.explore(lown=10, topn=15, means=c(5, 10), var=10) # two-sample t-test. Effective sample sizes are 20 to 30 (10 to 15 per group)
ttest.explore(lown=10, topn=15, means=20, var=10) # one-sample t-test. Comparing if average is different from 0.
ttest.explore(lown=10, topn=15, means=20, var=10, mu=10, alternative="higher") # one-sample t-test. Comparing if average is higher than 10.
``` |

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