R/replext_t6.R

In npboottprm: Nonparametric Bootstrap Test with Pooled Resampling

#' Replicate and Extend Statistical Power Simulation Results for Paired Distributions
#'
#' This function is a wrapper around 'replext_t5_c1.1' and is specifically aimed
#' at replicating and extending statistical power simulation results from Table 6
#' cell 1.1 of the paper by Dwivedi et al. (2017).
#'
#' @param M1 Mean for the first group, default is 5.
#' @param S1 Standard deviation for the first group, default is 1.
#' @param M2 Mean for the second group, default is 7.
#' @param S2 Standard deviation for the second group, default is 1.
#' @param Sk1 Skewness parameter for the first group, default is 0 (normal distribution).
#' @param Sk2 Skewness parameter for the second group, default is 0 (normal distribution).
#' @param correl Correlation between the two groups, default is 0.8.
#' @param n Vector of sample sizes for the paired groups.
#' @param n_simulations Number of simulations to run, default is 10,000.
#' @param nboot Number of bootstrap samples, default is 1000.
#' @param conf.level Confidence level for calculating p-value thresholds, default is 0.95.
#'
#' @return A data frame with columns for each sample size and
#'         the proportions of significant p-values for each test (PT, NPBTT, WRST, PTT),
#'         similar to 'replext_t5_c1.1' but with differing standard deviations for the groups.
#'
#' @examples
#' replext_t6_c1.1(n = c(10), n_simulations = 1)
#'
#' @references
#' Dwivedi AK, Mallawaarachchi I, Alvarado LA. Analysis of small sample size
#' studies using nonparametric bootstrap test with pooled resampling method.
#' Stat Med. 2017 Jun 30;36(14):2187-2205. doi: 10.1002/sim.7263. Epub 2017 Mar 9. PMID: 28276584.
#'
#' @export
replext_t6_c1.1 <- function(M1 = 5, S1 = 1, M2 = 7, S2 = 1, Sk1 = 0, Sk2 = 0,
                            correl = 0.8,
                            n = c(3,4,5,6,7,8,9,10,15,25,50,100),
                            n_simulations = 10000, nboot = 1000,
                            conf.level = 0.95){

  replext_t5_c1.1(M1, S1, M2, S2, Sk1, Sk2, correl, n, n_simulations, nboot,
                  conf.level)

}

#' Replicate and Extend Statistical Power Simulation Results for Paired Distributions with Different Variances
#'
#' This function is a wrapper around 'replext_t5_c1.1' and is specifically aimed
#' at replicating and extending statistical power simulation results from Table 6
#' cell 1.2 of the paper by Dwivedi et al. (2017).
#'
#' @param M1 Mean for the first group, default is 5.
#' @param S1 Standard deviation for the first group, default is 1.
#' @param M2 Mean for the second group, default is 7.
#' @param S2 Standard deviation for the second group, default is 3.
#' @param Sk1 Skewness parameter for the first group, default is 0 (normal distribution).
#' @param Sk2 Skewness parameter for the second group, default is 0 (normal distribution).
#' @param correl Correlation between the two groups, default is 0.8.
#' @param n Vector of sample sizes for the paired groups.
#' @param n_simulations Number of simulations to run, default is 10,000.
#' @param nboot Number of bootstrap samples, default is 1000.
#' @param conf.level Confidence level for calculating p-value thresholds, default is 0.95.
#'
#' @return A data frame with columns for each sample size and
#'         the proportions of significant p-values for each test (PT, NPBTT, WRST, PTT),
#'         similar to 'replext_t5_c1.1' but with differing standard deviations for the groups.
#'
#' @examples
#' replext_t6_c1.2(n = c(10), n_simulations = 1)
#'
#' @references
#' Dwivedi AK, Mallawaarachchi I, Alvarado LA. Analysis of small sample size
#' studies using nonparametric bootstrap test with pooled resampling method.
#' Stat Med. 2017 Jun 30;36(14):2187-2205. doi: 10.1002/sim.7263. Epub 2017 Mar 9. PMID: 28276584.
#'
#' @export
replext_t6_c1.2 <- function(M1 = 5, S1 = 1, M2 = 7, S2 = 3, Sk1 = 0, Sk2 = 0,
                            correl = 0.8,
                            n = c(3,4,5,6,7,8,9,10,15,25,50,100),
                            n_simulations = 10000, nboot = 1000,
                            conf.level = 0.95){

  replext_t5_c1.1(M1, S1, M2, S2, Sk1, Sk2, correl, n, n_simulations, nboot,
                  conf.level)

}

#' Replicate and Extend Statistical Power Simulation Results for Paired Distributions with Skewness
#'
#' This function, serving as a wrapper around 'replext_t5_c1.1', is designed to
#' replicate and extend the statistical power simulation results from Table 6
#' cell 1.3 of the paper by Dwivedi et al. (2017).
#'
#' @param M1 Mean for the first group, default is 5.
#' @param S1 Standard deviation for the first group, default is 1.
#' @param M2 Mean for the second group, default is 7.
#' @param S2 Standard deviation for the second group, default is 1.
#' @param Sk1 Skewness parameter for the first group, default is 0.5.
#' @param Sk2 Skewness parameter for the second group, default is 0.5.
#' @param correl Correlation between the two groups, default is 0.8.
#' @param n Vector of sample sizes for the paired groups.
#' @param n_simulations Number of simulations to run, default is 10,000.
#' @param nboot Number of bootstrap samples, default is 1000.
#' @param conf.level Confidence level for calculating p-value thresholds, default is 0.95.
#'
#' @return A data frame with columns for each sample size and
#'         the proportions of significant p-values for each test (PT, NPBTT, WRST, PTT),
#'         similar to 'replext_t5_c1.1' but with skewness parameters for the groups.
#'
#' @examples
#' replext_t6_c1.3(n = c(10), n_simulations = 1)
#'
#' @references
#' Dwivedi AK, Mallawaarachchi I, Alvarado LA. Analysis of small sample size
#' studies using nonparametric bootstrap test with pooled resampling method.
#' Stat Med. 2017 Jun 30;36(14):2187-2205. doi: 10.1002/sim.7263. Epub 2017 Mar 9. PMID: 28276584.
#'
#' @export
replext_t6_c1.3 <- function(M1 = 5, S1 = 1, M2 = 7, S2 = 1, Sk1 = 0.5, Sk2 = 0.5,
                            correl = 0.8,
                            n = c(3,4,5,6,7,8,9,10,15,25,50,100),
                            n_simulations = 10000, nboot = 1000,
                            conf.level = 0.95){

  replext_t5_c1.1(M1, S1, M2, S2, Sk1, Sk2, correl, n, n_simulations, nboot,
                  conf.level)

}

#' Replicate and Extend Statistical Power Simulation Results for Paired Distributions with Skewness and Different Variances
#'
#' This function is a wrapper around 'replext_t5_c1.1', targeting the replication
#' and extension of statistical power simulation results from Table 6 cell 2.1 of
#' the paper by Dwivedi et al. (2017).
#'
#' @param M1 Mean for the first group, default is 5.
#' @param S1 Standard deviation for the first group, default is 1.
#' @param M2 Mean for the second group, default is 7.
#' @param S2 Standard deviation for the second group, default is 3.
#' @param Sk1 Skewness parameter for the first group, default is 0.5 (indicating skew normal distribution).
#' @param Sk2 Skewness parameter for the second group, default is 0.5 (indicating skew normal distribution).
#' @param correl Correlation between the two groups, default is 0.8.
#' @param n Vector of sample sizes for the paired groups.
#' @param n_simulations Number of simulations to run, default is 10,000.
#' @param nboot Number of bootstrap samples, default is 1000.
#' @param conf.level Confidence level for calculating p-value thresholds, default is 0.95.
#'
#' @return A data frame with columns for each sample size and
#'         the proportions of significant p-values for each test (PT, NPBTT, WRST, PTT),
#'         similar to 'replext_t5_c1.1' but with variations in standard deviations and skewness.
#'
#' @examples
#' replext_t6_c2.1(n = c(10), n_simulations = 1)
#'
#' @references
#' Dwivedi AK, Mallawaarachchi I, Alvarado LA. Analysis of small sample size
#' studies using nonparametric bootstrap test with pooled resampling method.
#' Stat Med. 2017 Jun 30;36(14):2187-2205. doi: 10.1002/sim.7263. Epub 2017 Mar 9. PMID: 28276584.
#'
#' @export
replext_t6_c2.1 <- function(M1 = 5, S1 = 1, M2 = 7, S2 = 3, Sk1 = 0.5, Sk2 = 0.5,
                            correl = 0.8,
                            n = c(3,4,5,6,7,8,9,10,15,25,50,100),
                            n_simulations = 10000, nboot = 1000,
                            conf.level = 0.95){

  replext_t5_c1.1(M1, S1, M2, S2, Sk1, Sk2, correl, n, n_simulations, nboot,
                  conf.level)

}

#' Replicate and Extend Statistical Power Simulation Results for Paired Distributions with Different Skewness Levels
#'
#' This function is a specialized version of 'replext_t5_c1.1', tailored to
#' replicate and extend the statistical power simulation results from Table 6
#' cell 2.2 of the paper by Dwivedi et al. (2017).
#'
#' @param M1 Mean for the first group, default is 5.
#' @param S1 Standard deviation for the first group, default is 1.
#' @param M2 Mean for the second group, default is 7.
#' @param S2 Standard deviation for the second group, default is 1.
#' @param Sk1 Skewness parameter for the first group, default is 0.2.
#' @param Sk2 Skewness parameter for the second group, default is 0.8.
#' @param correl Correlation between the two groups, default is 0.8.
#' @param n Vector of sample sizes for the paired groups.
#' @param n_simulations Number of simulations to run, default is 10,000.
#' @param nboot Number of bootstrap samples, default is 1000.
#' @param conf.level Confidence level for calculating p-value thresholds, default is 0.95.
#'
#' @return A data frame with columns for each sample size and
#'         the proportions of significant p-values for each test (PT, NPBTT, WRST, PTT),
#'         with a focus on differing skewness levels between the two groups.
#'
#' @examples
#' replext_t6_c2.2(n = c(10), n_simulations = 1)
#'
#' @references
#' Dwivedi AK, Mallawaarachchi I, Alvarado LA. Analysis of small sample size
#' studies using nonparametric bootstrap test with pooled resampling method.
#' Stat Med. 2017 Jun 30;36(14):2187-2205. doi: 10.1002/sim.7263. Epub 2017 Mar 9. PMID: 28276584.
#'
#' @export
replext_t6_c2.2 <- function(M1 = 5, S1 = 1, M2 = 7, S2 = 1, Sk1 = 0.2, Sk2 = 0.8,
                            correl = 0.8,
                            n = c(3,4,5,6,7,8,9,10,15,25,50,100),
                            n_simulations = 10000, nboot = 1000,
                            conf.level = 0.95){

  replext_t5_c1.1(M1, S1, M2, S2, Sk1, Sk2, correl, n, n_simulations, nboot,
                  conf.level)

}

#' Replicate and Extend Statistical Power Simulation Results for Paired Distributions with Varied Skewness and Standard Deviations
#'
#' `replext_t6_c2.3` is a wrapper function around `replext_t5_c1.1`, specifically
#' designed to replicate and extend the statistical power simulation results from
#' Table 6 cell 2.3 of the paper by Dwivedi et al. (2017).
#'
#' @param M1 Mean for the first group, default is 5.
#' @param S1 Standard deviation for the first group, default is 1.
#' @param M2 Mean for the second group, default is 7.
#' @param S2 Standard deviation for the second group, default is 3.
#' @param Sk1 Skewness parameter for the first group, default is 0.2.
#' @param Sk2 Skewness parameter for the second group, default is 0.8.
#' @param correl Correlation between the two groups, default is 0.8.
#' @param n Vector of sample sizes for the paired groups.
#' @param n_simulations Number of simulations to run, default is 10,000.
#' @param nboot Number of bootstrap samples, default is 1000.
#' @param conf.level Confidence level for calculating p-value thresholds, default is 0.95.
#'
#' @return A data frame with columns for each sample size and
#'         the proportions of significant p-values for each test (PT, NPBTT, WRST, PTT),
#'         focusing on variations in skewness and standard deviations between the groups.
#'
#' @examples
#' replext_t6_c2.3(n = c(10), n_simulations = 1)
#'
#' @references
#' Dwivedi AK, Mallawaarachchi I, Alvarado LA. Analysis of small sample size
#' studies using nonparametric bootstrap test with pooled resampling method.
#' Stat Med. 2017 Jun 30;36(14):2187-2205. doi: 10.1002/sim.7263. Epub 2017 Mar 9. PMID: 28276584.
#'
#' @export
replext_t6_c2.3 <- function(M1 = 5, S1 = 1, M2 = 7, S2 = 3, Sk1 = 0.2, Sk2 = 0.8,
                            correl = 0.8,
                            n = c(3,4,5,6,7,8,9,10,15,25,50,100),
                            n_simulations = 10000, nboot = 1000,
                            conf.level = 0.95){

  replext_t5_c1.1(M1, S1, M2, S2, Sk1, Sk2, correl, n, n_simulations, nboot,
                  conf.level)

}