## ----setup, include = FALSE--------------------------------------------------- if (requireNamespace("kableExtra", quietly = TRUE)) library(kableExtra) knitr::opts_chunk$set( collapse = TRUE, comment = "#>", error = TRUE ) ## ----------------------------------------------------------------------------- library(SingleCaseES) ## ----------------------------------------------------------------------------- args(NAP) ## ----------------------------------------------------------------------------- A <- c(20, 20, 26, 25, 22, 23) B <- c(28, 25, 24, 27, 30, 30, 29) ## ----------------------------------------------------------------------------- NAP(A_data = A, B_data = B) ## ----------------------------------------------------------------------------- phase <- c(rep("A", 6), rep("B", 7)) phase ## ----------------------------------------------------------------------------- outcome_dat <- c(A, B) outcome_dat ## ----------------------------------------------------------------------------- NAP(condition = phase, outcome = outcome_dat) ## ----------------------------------------------------------------------------- phase2 <- c(rep("A", 5), rep("B", 5), rep("C",3)) NAP(condition = phase2, outcome = outcome_dat) ## ----------------------------------------------------------------------------- phase_rev <- c(rep("B", 7), rep("A", 6)) outcome_rev <- c(B, A) NAP(condition = phase_rev, outcome = outcome_rev, baseline_phase = "A") ## ----------------------------------------------------------------------------- NAP(condition = phase2, outcome = outcome_dat, baseline_phase = "A", intervention_phase = "C") NAP(condition = phase2, outcome = outcome_dat, baseline_phase = "B", intervention_phase = "C") ## ----------------------------------------------------------------------------- NAP(A_data = A, B_data = B, improvement = "decrease") ## ----------------------------------------------------------------------------- NAP(A_data = A, B_data = B, SE = "unbiased") NAP(A_data = A, B_data = B, SE = "Hanley") NAP(A_data = A, B_data = B, SE = "null") NAP(A_data = A, B_data = B, SE = "none") ## ----------------------------------------------------------------------------- NAP(A_data = A, B_data = B) NAP(A_data = A, B_data = B, confidence = .99) NAP(A_data = A, B_data = B, confidence = .90) NAP(A_data = A, B_data = B, confidence = NULL) ## ----------------------------------------------------------------------------- Tau(A_data = A, B_data = B) Tau_BC(A_data = A, B_data = B) PND(A_data = A, B_data = B) PEM(A_data = A, B_data = B) PAND(A_data = A, B_data = B) IRD(A_data = A, B_data = B) Tau_U(A_data = A, B_data = B) ## ----------------------------------------------------------------------------- SMD(A_data = A, B_data = B, improvement = "increase") SMD(A_data = A, B_data = B, improvement = "decrease") ## ----------------------------------------------------------------------------- SMD(A_data = A, B_data = B, std_dev = "baseline") SMD(A_data = A, B_data = B, std_dev = "pool") ## ----------------------------------------------------------------------------- A <- c(20, 20, 26, 25, 22, 23) B <- c(28, 25, 24, 27, 30, 30, 29) LRRi(A_data = A, B_data = B, scale = "percentage") LRRi(A_data = A, B_data = B, improvement = "decrease", scale = "percentage") ## ----------------------------------------------------------------------------- A <- c(20, 20, 26, 25, 22, 23) B <- c(28, 25, 24, 27, 30, 30, 29) LRRi(A_data = A, B_data = B, scale = "count") LRRi(A_data = A, B_data = B, scale = "count", improvement = "decrease") ## ----------------------------------------------------------------------------- A <- c(0, 0, 0, 0) B <- c(28, 25, 24, 27, 30, 30, 29) LRRd(A_data = A, B_data = B, scale = "rate") LRRd(A_data = A, B_data = B, scale = "rate", observation_length = 30) ## ----------------------------------------------------------------------------- LRRd(A_data = A, B_data = B, scale = "percentage") LRRd(A_data = A, B_data = B, scale = "percentage", intervals = 180) ## ----------------------------------------------------------------------------- A_pct <- c(20, 20, 25, 25, 20, 25) B_pct <- c(30, 25, 25, 25, 35, 30, 25) LOR(A_data = A_pct, B_data = B_pct, scale = "percentage") LOR(A_data = A_pct/100, B_data = B_pct/100, scale = "proportion") LOR(A_data = A_pct, B_data = B_pct, scale = "count") LOR(A_data = A_pct, B_data = B_pct, scale = "proportion") ## ----------------------------------------------------------------------------- LOR(A_data = A_pct, B_data = B_pct, scale = "percentage", improvement = "increase") LOR(A_data = A_pct, B_data = B_pct, scale = "percentage", improvement = "decrease") ## ----------------------------------------------------------------------------- LOR(A_data = c(0,0,0), B_data = B_pct, scale = "percentage") LOR(A_data = c(0,0,0), B_data = B_pct, scale = "percentage", intervals = 20) ## ----------------------------------------------------------------------------- A <- c(20, 20, 26, 25, 22, 23) B <- c(28, 25, 24, 27, 30, 30, 29) calc_ES(A_data = A, B_data = B, ES = c("NAP","PND","Tau-U")) ## ----------------------------------------------------------------------------- phase <- c(rep("A", length(A)), rep("B", length(B))) outcome <- c(A, B) calc_ES(condition = phase, outcome = outcome, baseline_phase = "A", ES = c("NAP","PND","Tau-U")) ## ----------------------------------------------------------------------------- calc_ES(A_data = A, B_data = B, ES = "SMD") ## ----------------------------------------------------------------------------- calc_ES(A_data = A, B_data = B, ES = c("NAP", "PND", "Tau-U")) ## ----------------------------------------------------------------------------- calc_ES(A_data = A, B_data = B, ES = "all") ## ----------------------------------------------------------------------------- calc_ES(A_data = A, B_data = B, ES = "NOM") ## ----------------------------------------------------------------------------- calc_ES(A_data = A, B_data = B, ES = "parametric") ## ----------------------------------------------------------------------------- calc_ES(A_data = A, B_data = B) ## ----------------------------------------------------------------------------- calc_ES(A_data = A, B_data = B, ES = "NOM", improvement = "decrease") ## ----------------------------------------------------------------------------- calc_ES(A_data = A, B_data = B, ES = "NOM", improvement = "decrease", confidence = NULL) ## ----------------------------------------------------------------------------- calc_ES(A_data = A, B_data = B, ES = c("NAP","PND","SMD")) calc_ES(A_data = A, B_data = B, ES = c("NAP","PND","SMD"), format = "wide") ## ----------------------------------------------------------------------------- data(McKissick) ## ---- echo = FALSE------------------------------------------------------------ knitr::kable(head(McKissick, n = 10)) ## ----------------------------------------------------------------------------- data(Schmidt2007) ## ---- echo = FALSE------------------------------------------------------------ Schmidt_kable <- knitr::kable(head(subset(Schmidt2007,select = c(Case_pseudonym, Behavior_type, Session_number, Outcome, Condition, Phase_num, Metric, Session_length, direction, n_Intervals)), n = 10), longtable = TRUE) if (requireNamespace("kableExtra", quietly = TRUE)) { Schmidt_kable %>% kable_styling() %>% scroll_box(width = "100%") } else { Schmidt_kable } ## ----------------------------------------------------------------------------- args(batch_calc_ES) ## ----------------------------------------------------------------------------- mckissick_ES <- batch_calc_ES(dat = McKissick, grouping = Case_pseudonym, condition = Condition, outcome = Outcome, improvement = "decrease", ES = c("NAP", "PND")) ## ---- echo = FALSE------------------------------------------------------------ kable(mckissick_ES) ## ----------------------------------------------------------------------------- schmidt_ES <- batch_calc_ES( dat = Schmidt2007, grouping = c(Case_pseudonym, Behavior_type, Phase_num), condition = Condition, outcome = Outcome, improvement = direction, ES = c("NAP", "LRRi") ) ## ---- echo = FALSE------------------------------------------------------------ if (requireNamespace("kableExtra", quietly = TRUE)) { kable(schmidt_ES, digits = 3) %>% kable_styling() %>% scroll_box( width = "100%", height = "800px", fixed_thead = list(enabled = TRUE, background = "green") ) } else { knitr::kable(schmidt_ES, digits = 3) } ## ----------------------------------------------------------------------------- schmidt_ES_agg <- batch_calc_ES( dat = Schmidt2007, grouping = c(Case_pseudonym, Behavior_type), aggregate = Phase_num, condition = Condition, outcome = Outcome, improvement = direction, ES = "NAP" ) ## ---- echo = FALSE------------------------------------------------------------ kable(schmidt_ES_agg) %>% kable_styling() ## ----------------------------------------------------------------------------- schmidt_ES_agg <- batch_calc_ES( dat = Schmidt2007, grouping = c(Case_pseudonym, Behavior_type), aggregate = Phase_num, weighting = "equal", condition = Condition, outcome = Outcome, improvement = direction, ES = "NAP" ) ## ---- echo = FALSE------------------------------------------------------------ if (requireNamespace("kableExtra", quietly = TRUE)) { kable(schmidt_ES_agg, digits = 3) %>% kable_styling() } else { knitr::kable(schmidt_ES_agg, digits = 3) } ## ----------------------------------------------------------------------------- mckissick_ES <- batch_calc_ES(dat = McKissick, grouping = Case_pseudonym, condition = Condition, outcome = Outcome, improvement = "decrease", scale = "count", observation_length = 20, ES = "parametric") ## ---- echo = FALSE------------------------------------------------------------ knitr::kable(mckissick_ES, digits = 3) ## ----------------------------------------------------------------------------- schmidt_ES <- batch_calc_ES(dat = Schmidt2007, grouping = c(Case_pseudonym, Behavior_type, Phase_num), condition = Condition, outcome = Outcome, improvement = direction, scale = Metric, observation_length = Session_length, intervals = n_Intervals, ES = c("parametric")) ## ---- echo = FALSE------------------------------------------------------------ if (requireNamespace("kableExtra", quietly = TRUE)) { kable(schmidt_ES, digits = 3) %>% kable_styling() %>% scroll_box( width = "100%", height = "800px", fixed_thead = list(enabled = TRUE, background = "green") ) } else { knitr::kable(schmidt_ES, digits = 3) } ## ----------------------------------------------------------------------------- mckissick_wide_ES <- batch_calc_ES( dat = McKissick, grouping = Case_pseudonym, condition = Condition, outcome = Outcome, improvement = "decrease", ES = c("NAP", "PND"), format = "wide" ) ## ---- echo = FALSE------------------------------------------------------------ knitr::kable(mckissick_wide_ES) ## ----------------------------------------------------------------------------- batch_calc_ES(dat = McKissick, grouping = Case_pseudonym, condition = Condition, outcome = Outcome, improvement = "decrease", scale = "count", observation_length = 20, ES = c("LRRi","LOR"), warn = FALSE) ## ----------------------------------------------------------------------------- batch_calc_ES(dat = McKissick, grouping = Case_pseudonym, condition = Condition, outcome = Outcome, ES = "SMD", improvement = "decrease") batch_calc_ES(dat = McKissick, grouping = Case_pseudonym, condition = Condition, outcome = Outcome, ES = "SMD", improvement = "decrease", std_dev = "pool") ## ----------------------------------------------------------------------------- batch_calc_ES(dat = McKissick, grouping = Case_pseudonym, condition = Condition, outcome = Outcome, ES = "parametric", improvement = "decrease", scale = Procedure, observation_length = Session_length, bias_correct = FALSE, warn = FALSE) ## ----------------------------------------------------------------------------- batch_calc_ES(dat = McKissick, grouping = Case_pseudonym, condition = Condition, outcome = Outcome, ES = "parametric", improvement = "decrease", scale = Procedure, observation_length = Session_length, confidence = NULL, warn = FALSE)