Description Usage Arguments Value References Examples

Given a random-effects ANOVA model, *Y_{ij}=μ+u_i+ε_{ij}*
with *u_i\sim N(0,σ_b^2), ε_{ij}\sim N(0,σ_w^2)*, we
want to make inference on the total absolute variation parameter, *ρ^2=μ^2+σ_b^2+σ_w^2*.
Large-sample Z-tests can be conducted based on approximating *∑_{i,j}Y_{ij}^2* with a normal distribution.

1 |

`ng` |
sample sizes for each factor level |

`mus` |
sample mean values at each factor level |

`sse` |
total error sum of squares (sum of within-group sample variances) |

`alpha` |
desired CI coefficient |

`rho0` |
the null threshold value to test |

`REML` |
whether we use REML or MLE, default to TRUE |

- p.value
Z-score and Z-Wald test p-values

- par0
estimated parameters, test statistic and its variance, and CI for

*ρ^2*, under the null hypothesis- pars
estimated parameters, test statistic and its variance, and CI for

*ρ^2*

Ndikintum, N. K. and Rao, M. (2016). A Special Inference Problem in Repeated Measures Design Test of Statistical Hypothesis on Accuracy Root Mean Square Application to Pulse Oximetry Studies. Statistics in Biopharmaceutical Research 8, 60–76.

Pennello, G. A. (2002). Sample size for paired repeated measures designs of method compari- son studies: Application to pulse oximetry. ASA Proceedings of the Joint Statistical Meetings pages 2671–2675.

Pennello, G. A. (2003). Comparing monitoring devices when a gold standard in unavailable: Application to pulse oximeters. ASA Proceedings of the Joint Statistical Meetings pages 3256–3263.

Bai,Y., Wang,Z., Lystig,T., and Wu,B. (2019) Efficient and powerful equivalency test on combined mean and variance with application to diagnostic device comparison studies. arXiv:1908.07979

1 2 3 4 5 6 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.