inst/doc/Using-SingleCaseES.R

In SingleCaseES: A Calculator for Single-Case Effect Sizes

## ----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)