Description Usage Arguments Value Examples

Use `mdes.ird()`

to calculate minimum detectable effect size and `power.ird()`

to calculate statistical power. If higher level strata or fixed blocks exist, use `mdes.bird2f1()`

to calculate minimum detectable effect size, `power.bird2f1()`

to calculate statistical power, and `cosa.bird2f1()`

for bound constrained optimal sample size allocation (BCOSSA).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | ```
mdes.ird(score = NULL, dists = "normal", k1 = -6, k2 = 6,
order = 1, interaction = FALSE,
treat.lower = TRUE, cutoff = 0, p = NULL,
power = .80, alpha = .05, two.tailed = TRUE,
df = n1 - g1 - order * (1 + interaction) - 2,
r21 = 0, g1 = 0, rate.tp = 1, rate.cc = 0, n1)
power.ird(score = NULL, dists = "normal", k1 = -6, k2 = 6,
order = 1, interaction = FALSE,
treat.lower = TRUE, cutoff = 0, p = NULL,
es = .25, alpha = .05, two.tailed = TRUE,
df = n1 - g1 - order * (1 + interaction) - 2,
r21 = 0, g1 = 0, rate.tp = 1, rate.cc = 0, n1)
mdes.bird2f1(score = NULL, dists = "normal", k1 = -6, k2 = 6,
order = 1, interaction = FALSE,
treat.lower = TRUE, cutoff = 0, p = NULL,
power = .80, alpha = .05, two.tailed = TRUE,
df = n2 * (n1 - 2) - g1 - order * (1 + interaction),
r21 = 0, g1 = 0, rate.tp = 1, rate.cc = 0, n1, n2 = 1)
power.bird2f1(score = NULL, dists = "normal", k1 = -6, k2 = 6,
order = 1, interaction = FALSE,
treat.lower = TRUE, cutoff = 0, p = NULL,
es = .25, alpha = .05, two.tailed = TRUE,
df = n2 * (n1 - 2) - g1 - order * (1 + interaction),
r21 = 0, g1 = 0, rate.tp = 1, rate.cc = 0, n1, n2 = 1)
cosa.bird2f1(score = NULL, dists = "normal", k1 = -6, k2 = 6, rhots = NULL,
order = 1, interaction = FALSE,
treat.lower = TRUE, cutoff = 0, p = NULL,
cn1 = 0, cn2 = 0, cost = NULL,
n1 = NULL, n2 = NULL,
n0 = c(400, 5), p0 = .499,
constrain = "power", round = TRUE, max.power = FALSE,
local.solver = c("LBFGS", "SLSQP"),
power = .80, es = .25, alpha = .05, two.tailed = TRUE,
g1 = 0, r21 = 0)
``` |

`score` |
vector or list; an empirical score variable or an object with class 'score' returned from the |

`dists` |
character; distribution of the score variable, |

`k1` |
left truncation point for (uncentered) empirical, truncated normal, or uniform distribution. Ignored when |

`k2` |
right truncation point for (uncentered) empirical, truncated normal, or uniform distribution. Ignored when |

`order` |
integer >= 0; order of polynomial functional form specification for the score variable. |

`interaction` |
logical; if |

`rhots` |
obsolote; use |

`treat.lower` |
logical; if |

`cutoff` |
decision threshold. |

`p` |
proportion of units in the treatment condition. |

`power` |
statistical power (1 - |

`es` |
numeric > 0; effect size (Cohen's d). |

`alpha` |
probability of type I error ( |

`two.tailed` |
logical; |

`df` |
degrees of freedom. |

`g1` |
number of covariates. |

`r21` |
proportion of variance in the outcome explained by covariates. |

`rate.tp` |
treatment group participation rate. |

`rate.cc` |
control group crossover rate. |

`n1` |
sample size (per stratum or block, if exists). |

`n2` |
number of stratum or fixed blocks. |

`cn1` |
marginal cost per unit in treatment and control conditions, e.g. |

`cn2` |
marginal cost per stratum or fixed block. |

`cost` |
total cost or budget. Ignored when |

`constrain` |
character; constrains one of the |

`n0` |
starting value for |

`p0` |
starting value for |

`round` |
logical; |

`max.power` |
logical; |

`local.solver` |
subset of |

`parms` |
list of parameters used in the function. |

`df` |
degrees of freedom. |

`sse` |
standardized standard error. |

`cosa` |
BCOSSA solution. |

`mdes` |
minimum detectable effect size and (1 - |

`power` |
statistical power (1 - |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
score.obj <- inspect.score(rnorm(1000),
order = 1, interaction = FALSE,
cutoff = 0, k1 = -1, k2 = 1)
# single site (no blocks)
power.ird(score.obj, g1 = 0, r21 = 0,
es = 0.25, n = 100)
# with 5 blocks (note that r21 is modified but g1 remains the same)
power.bird2f1(score.obj, g1 = 0, r21 = .30,
es = 0.25, n1 = 100, n2 = 5)
# minimum required sample size for each block
cosa.bird2f1(score.obj, g1 = 0, r21 = .30,
n1 = NULL, n2 = 5)
``` |

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