## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set(collapse = TRUE, comment = "#>") library(BUCSS) ## ----------------------------------------------------------------------------- ss_buc_independent_t(t_observed = 3, n = 20, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_paired_t(t_observed = 3, N = 40, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_one_sample_t(t_observed = 3.2, N = 30, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_one_way_anova(F_observed = 5, N = 120, levels_A = 4, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_factorial_anova(F_observed = 5, N = 120, levels_A = 2, levels_B = 3, effect = "factor_B", alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_factorial_anova_general(F_observed = 5, N = 120, cells = 6, df_numerator = 2, df_denominator = 114, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_rm_anova(F_observed = 5, N = 60, levels_A = 2, levels_B = 3, effect = "factor_B", alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_rm_anova_general(F_observed = 6.5, N = 80, df_numerator = 1, alpha_prior = .05, alpha_planned = .05, assurance = .50, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_mixed_anova(F_observed = 5, N = 60, levels_between = 2, levels_within = 3, effect = "within", alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_mixed_anova_general(F_observed = 5, N = 90, df_numerator = 2, num_groups = 3, effect = "between_only", alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_rm_anova(F_observed = 6.5, N = 30, levels_A = 3, epsilon = 1.73 / 2) ## ----------------------------------------------------------------------------- ss_buc_rm_anova(F_observed = 6.5, N = 30, levels_A = 3) ## ----------------------------------------------------------------------------- eps <- c(1, .95, .9, .85, .8, .75) sapply(eps, function(e) { res <- ss_buc_rm_anova(F_observed = 6.5, N = 30, levels_A = 3, epsilon = e) res$value[res$term == "necessary_N"] }) ## ----------------------------------------------------------------------------- ss_buc_reg_coef(t_observed = 3, N = 150, p = 3, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_R2(F_observed = 5, N = 150, p = 4, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_reg_joint(F_observed = 5, N = 150, p = 4, p_joint = 2, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_R2(F_observed = 5, N = 150, p = 4, random_predictors = TRUE) ## ----------------------------------------------------------------------------- ss_buc_correlation(r_observed = .35, N = 100, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- t_equivalent <- .35 * sqrt(98 / (1 - .35^2)) by_correlation <- ss_buc_correlation(t_observed = t_equivalent, N = 100) by_regression <- ss_buc_reg_coef(t_observed = t_equivalent, N = 100, p = 1, random_predictors = FALSE) c(correlation = by_correlation$value[by_correlation$term == "necessary_N"], regression = by_regression$value[by_regression$term == "necessary_N"]) ## ----------------------------------------------------------------------------- ss_buc_ancova(F_observed = 6, N = 120, cells = 3, n_covariates = 2, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_manova(F_observed = 4.5, N = 80, p_variables = 3, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- ss_buc_chisq_diff(chisq_observed = 9.5, N = 250, df_full = 42, df_restricted = 43, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----error = TRUE------------------------------------------------------------- try({ ss_buc_chisq_diff(chisq_observed = 9.5, N = 250, df_full = 0, df_restricted = 43) }) ## ----------------------------------------------------------------------------- ss_buc_welch_t(t_observed = 3, n_1 = 40, n_2 = 55, sd_ratio = 1.5, alpha_prior = .05, alpha_planned = .05, assurance = .80, desired_power = .80) ## ----------------------------------------------------------------------------- by_ratio <- ss_buc_welch_t(t_observed = 3, n_1 = 40, n_2 = 55, sd_ratio = 1.5) by_sds <- ss_buc_welch_t(t_observed = 3, n_1 = 40, n_2 = 55, sd_1 = 8.4, sd_2 = 12.6) by_vars <- ss_buc_welch_t(t_observed = 3, n_1 = 40, n_2 = 55, var_1 = 70.56, var_2 = 158.76) size_term <- "necessary_n_per_group" c(ratio = by_ratio$value[by_ratio$term == size_term], sds = by_sds$value[by_sds$term == size_term], vars = by_vars$value[by_vars$term == size_term]) ## ----------------------------------------------------------------------------- ratios <- c(1, 1.5, 2, 2.5, 3) sapply(ratios, function(r) { res <- ss_buc_welch_t(t_observed = 3, n_1 = 40, n_2 = 55, sd_ratio = r) res$value[res$term == "necessary_n_per_group"] })