Package 'scan'

Description

Adds dummy variables to an scdf for calculating piecewise linear models.

Usage

add_dummy_variables(
  scdf,
  model = c("W", "H-M", "B&L-B"),
  contrast_level = c("first", "preceding"),
  contrast_slope = c("first", "preceding")
)

Arguments

Examples

add_dummy_variables(
 scdf = exampleABC, 
 model = "W", 
 contrast_level = "first", 
 contrast_slope = "first"
)

Description

Merges variables with corresponding case names from a data.frame with an scdf file

Usage

add_l2(scdf, data_l2, cvar = "case")

Arguments

Details

This function is mostly used in combination with the hplm() function.

Value

An scdf

See Also

hplm()

Other data manipulation functions: as.data.frame.scdf(), as_scdf(), fill_missing(), moving_median(), outlier(), ranks(), rescale(), scdf(), select_cases(), set_vars(), shift(), smooth_cases(), standardize(), truncate_phase()

Examples

Leidig2018 |> add_l2(Leidig2018_l2)

Description

ANOVA Table for Piecewise Linear Models

Usage

## S3 method for class 'sc_plm'
anova(object, ...)

## S3 method for class 'sc_hplm'
anova(object, ...)

Arguments

Examples

mod1 <- plm(exampleAB$Johanna)
mod2 <- plm(exampleAB$Johanna, level = FALSE, slope = FALSE)
anova(mod1, mod2)

## For multilevel models:
# mod1 <- hplm(exampleAB_50)
# mod2 <- hplm(exampleAB_50, slope = FALSE)
# anova(mod1, mod2)

Description

Converts a data frame to an scdf object.

Usage

as_scdf(
  object,
  cvar = "case",
  pvar = "phase",
  dvar = "values",
  mvar = "mt",
  phase_names = NULL,
  sort_cases = FALSE
)

Arguments

Value

An scdf.

See Also

Other data manipulation functions: add_l2(), as.data.frame.scdf(), fill_missing(), moving_median(), outlier(), ranks(), rescale(), scdf(), select_cases(), set_vars(), shift(), smooth_cases(), standardize(), truncate_phase()

Description

The as.data.frame function transposes an scdf into one long data frame. Additionally, a data frame can be merged that includes level 2 data of the subjects. This might be helpful to prepare data to be used with other packages than scan.

Usage

## S3 method for class 'scdf'
as.data.frame(x, ..., l2 = NULL, id = "case")

Arguments

Value

Returns one data frame with data of all single-cases structured by the case variable.

Author(s)

Juergen Wilbert

See Also

Other data manipulation functions: add_l2(), as_scdf(), fill_missing(), moving_median(), outlier(), ranks(), rescale(), scdf(), select_cases(), set_vars(), shift(), smooth_cases(), standardize(), truncate_phase()

Examples

## Convert the list of three single-case data frames from Grosche (2011)
### into one long data frame
Grosche2011
Grosche2011_long <- as.data.frame(Grosche2011)
Grosche2011_long

## Combine an scdf with data for l2
Leidig2018_long <- as.data.frame(Leidig2018, l2 = Leidig2018_l2)
names(Leidig2018_long)
summary(Leidig2018_long)

Description

The autocorr function calculates autocorrelations within each phase and across all phases.

Usage

autocorr(data, dvar, pvar, mvar, lag_max = 3, lag.max, ...)

Arguments

Value

A data frame containing separate autocorrelations for each phase and for all phases (for each single-case). If lag_max exceeds the length of a phase minus one, NA is returned for this cell.

Author(s)

Juergen Wilbert

See Also

acf()

Other regression functions: bplm(), corrected_tau(), hplm(), mplm(), plm(), trend()

Examples

## Compute autocorrelations for a list of four single-cases up to lag 2.
autocorr(Huber2014, lag_max = 2)

Description

This function applies a given function to each case of a multiple case scdf, returning a list of the output of each function call.

Usage

batch_apply(scdf, fn, simplify = FALSE)

Arguments

Value

A list of the output of each function call.

Examples

batch_apply(exampleAB, coef(plm(.)))

Description

Calculates a standardized mean difference from a multilevel model as described in Pustejovsky et al. (2014)

Usage

between_smd(data, include_residuals = TRUE, model = "frequentist", ...)

## S3 method for class 'sc_bcsmd'
print(x, digits = 2, ...)

Arguments

Details

The BC-SMD is calculate as ⁠BC-SMD = Phase difference / sqrt(residual + random_intercept)⁠. This is most closely related to Cohen's d. If you want to have the most exact estimation based on the between case variance, you have to exclude the residual variance by setting the argument include_residuals = FALSE you get ⁠BC-SMD = Phase difference / sqrt(random_intercept)⁠. The 'base' model only includes the phase level as a predictor like originally proposed by Hedges et al. Whereas the 'Full plm' model includes the trend and the phase slope as additional predictors.

Value

An object of class sc_bcsmd.

Functions

• print(sc_bcsmd): Print results

References

Pustejovsky, J. E., Hedges, L. V., & Shadish, W. R. (2014). Design-Comparable Effect Sizes in Multiple Baseline Designs: A General Modeling Framework. Journal of Educational and Behavioral Statistics, 39(5), 368–393. https://doi.org/10.3102/1076998614547577

Examples

## Create a example scdf:
des <- design(
  n = 150,
  phase_design = list(A1 = 10, B1 = 10, A2 = 10, B2 = 10, C = 10),
  level = list(B1 = 1, A2 = 0, B2 = 1, C = 1),
  rtt = 0.7,
  random_start_value = TRUE
)
study <- random_scdf(des)

## Standard BC-SMD return:
between_smd(study)

## Specify the model and provide an hplm object:
model <- hplm(study, contrast_level = "preceding", slope = FALSE,  trend = FALSE)
between_smd(model)

## excluding the residuals gives a more accruate estimation:
between_smd(model, include_residuals = FALSE)

Description

Computes a bayesian (hierarchical) piecewise linear model based on a Markov chain Monte Carlo sampler.

Usage

bplm(
  data,
  dvar,
  pvar,
  mvar,
  model = c("W", "H-M", "B&L-B"),
  contrast_level = c("first", "preceding"),
  contrast_slope = c("first", "preceding"),
  trend = TRUE,
  level = TRUE,
  slope = TRUE,
  random_trend = FALSE,
  random_level = FALSE,
  random_slope = FALSE,
  fixed = NULL,
  random = NULL,
  update_fixed = NULL,
  ...
)

## S3 method for class 'sc_bplm'
print(x, digits = 3, ...)

## S3 method for class 'sc_bplm'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  round = 2,
  nice = TRUE,
  ...
)

Arguments

Value

An object of class sc_bplm.

Functions

• print(sc_bplm): Print results

• export(sc_bplm): Export results as html table (see export())

Author(s)

Juergen Wilbert

See Also

Other regression functions: autocorr(), corrected_tau(), hplm(), mplm(), plm(), trend()

Examples

# plm regression
bplm(example_A24)

# Multilevel plm regression with random intercept
bplm(exampleAB_50, nitt = 5000)

# Adding a random slope
bplm(exampleAB_50, random_level = TRUE, nitt = 5000)

Description

The cdc() function applies the Conservative Dual-Criterion Method (Fisher, Kelley, & Lomas, 2003) to scdf objects. It compares phase B data points to both phase A mean and trend (OLS, bi-split, tri-split) with an additional increase/decrease of .25 SD. A binomial test against a 50/50 distribution is computed and p-values below .05 are labeled "systematic change".

Usage

cdc(
  data,
  dvar,
  pvar,
  mvar,
  decreasing = FALSE,
  trend_method = c("OLS", "bisplit", "trisplit"),
  conservative = 0.25,
  phases = c(1, 2)
)

Arguments

Value

Author(s)

Timo Lueke

References

Fisher, W. W., Kelley, M. E., & Lomas, J. E. (2003). Visual Aids and Structured Criteria for Improving Visual Inspection and Interpretation of Single-Case Designs. Journal of Applied Behavior Analysis, 36, 387-406. https://doi.org/10.1901/jaba.2003.36-387

See Also

Other overlap functions: ird(), nap(), overlap(), pand(), pem(), pet(), pnd(), tau_u()

Examples

## Apply the CDC method (standard OLS line)
design <- design(n = 1, slope = 0.2)
dat <- random_scdf(design, seed = 42)
cdc(dat)

## Apply the CDC with Koenig's bi-split and an expected decrease in phase B.
cdc(exampleAB_decreasing, decreasing = TRUE, trend_method = "bisplit")

## Apply the CDC with Tukey's tri-split, comparing the first and fourth phase
cdc(exampleABAB, trend_method = "trisplit", phases = c(1,4))

## Apply the Dual-Criterion (DC) method (i.e., mean and trend without
##shifting).
cdc(
 exampleAB_decreasing,
 decreasing = TRUE,
 trend_method = "bisplit",
 conservative = 0
)

Description

Extract coefficients from plm/hplm objects

Usage

## S3 method for class 'sc_plm'
coef(object, ...)

Arguments

Value

data frame with coefficient table

Examples

coefficients(plm(exampleAB$Johanna))

Description

Combine single-case data frames

Usage

combine(..., dvar = NULL, pvar = NULL, mvar = NULL, info = NULL, author = NULL)

## S3 method for class 'scdf'
c(...)

Arguments

Value

A scdf. If not set differently, the attributes of this scdf are copied from the first scdf provided (i.e the first argument of the function).

Description

Converts an scdf object into R code

Usage

convert(
  scdf,
  file = "",
  study_name = "study",
  case_name = "case",
  inline = FALSE,
  indent = 2,
  silent = FALSE
)

Arguments

Value

Returns a string (invisible).

See Also

Other io-functions: read_scdf(), write_scdf()

Examples

filename <- tempfile()
convert(exampleABC, file = filename)
source(filename)
all.equal(study, exampleABC)
unlink(filename)

Description

Kendall's tau correlation for the dependent variable and the phase variable is calculated after correcting for a baseline trend.

Usage

corrected_tau(
  data,
  dvar,
  pvar,
  mvar,
  phases = c(1, 2),
  alpha = 0.05,
  continuity = FALSE,
  repeated = FALSE,
  tau_method = c("b", "a")
)

Arguments

Details

This method has been proposed by Tarlow (2016). The baseline data are checked for a significant autocorrelation (based on Kendall's Tau). If so, a non-parametric Theil-Sen regression is applied for the baseline data where the dependent values are regressed on the measurement time. The resulting slope information is then used to predict data of the B-phase. The dependent variable is now corrected for this baseline trend and the residuals of the Theil-Sen regression are taken for further calculations. Finally, Kendall's tau is calculated for the dependent variable and the dichotomous phase variable. The function here provides two extensions to this procedure: The more accurate Siegel repeated median regression is applied when repeated = TRUE and a continuity correction is applied when continuity = TRUE.

References

Tarlow, K. R. (2016). An Improved Rank Correlation Effect Size Statistic for Single-Case Designs: Baseline Corrected Tau. Behavior Modification, 41(4), 427–467. https://doi.org/10.1177/0145445516676750

See Also

Other regression functions: autocorr(), bplm(), hplm(), mplm(), plm(), trend()

Examples

dat <- scdf(c(A = 33,25,17,25,14,13,15, B = 15,16,16,5,7,9,6,5,3,3,8,11,7))
corrected_tau(dat)

Description

The describe() function provides common descriptive statistics for single-case data.

Usage

describe(data, dvar, pvar, mvar)

Arguments

Details

n = number of measurements; mis = number of missing vaues; m = mean; md = median; sd = standard deviation; mad = median average deviation; min = minimum; max = maximum; trend = weight of depended variable regressed on time (values ~ mt).

Value

A list containing a data frame of descriptive statistics (descriptives); the cse design (design); the number of cases (N)

Author(s)

Juergen Wilbert

See Also

overlap(), plot.scdf()

Examples

## Descriptive statistics for a study of three single-cases
describe(Grosche2011)

## Descriptives of a three phase design
describe(exampleABC)

## Write descriptive statistics to .csv-file
study <- describe(Waddell2011)
write.csv(study$descriptives, file = tempfile())

Description

Generates a parameter list used for generating multiple random single-cases. This is used within the random_scdf function and the power_test function and for other Monte-Carlo tasks.

Usage

design(
  n = 1,
  phase_design = list(A = 5, B = 15),
  trend = 0,
  level = list(0),
  slope = list(0),
  start_value = 50,
  s = 10,
  rtt = 0.8,
  extreme_prop = list(0),
  extreme_range = c(-4, -3),
  missing_prop = 0,
  distribution = c("normal", "gaussian", "poisson", "binomial"),
  random_start_value = FALSE,
  n_trials = NULL,
  mt = NULL,
  B_start = NULL,
  m,
  phase.design,
  MT,
  B.start,
  extreme.p,
  extreme.d,
  missing.p
)

Arguments

Value

An object of class sc_design.

Author(s)

Juergen Wibert

Examples

## Create random single-case data and inspect it
 design <- design(
   n = 3, rtt = 0.75, slope = 0.1, extreme_prop = 0.1,
   missing_prop = 0.1
 )
 dat <- random_scdf(design, round = 1, random.names = TRUE, seed = 123)
 describe(dat)

 ## And now have a look at poisson-distributed data
 design <- design(
   n = 3, B_start = c(6, 10, 14), mt = c(12, 20, 22), start_value = 10,
   distribution = "poisson", level = -5, missing_prop = 0.1
 )
 dat <- random_scdf(design, seed = 1234)
 pand(dat, decreasing = TRUE)

Description

This functions takes an scdf and extracts design parameters. The resulting object can be used to randomly create new scdf files with the same underlying parameters. This is useful for Monte-Carlo studies and bootstrapping procedures.

Usage

estimate_design(
  data,
  dvar,
  pvar,
  mvar,
  s = NULL,
  rtt = NULL,
  overall_effects = FALSE,
  overall_rtt = TRUE,
  model = "JW",
  ...
)

Arguments

Value

A list of parameters for each single-case. Parameters include name, length, and starting measurement time of each phase, trend, level, and slope effects for each phase, start value, standard deviation, and reliability for each case.

Examples

# create a random scdf with predefined parameters
set.seed(1234)
design <- design(
  n = 10, trend = -0.02,
  level = list(0, 1), rtt = 0.8,
  s = 1
)
scdf<- random_scdf(design)

# Estimate the parameters based on the scdf and create a new random scdf
# based on these estimations
design_est <- estimate_design(scdf, rtt = 0.8)
scdf_est <- random_scdf(design_est)

# Analyze both datasets with an hplm model. See how similar the estimations
# are:
hplm(scdf, slope = FALSE)
hplm(scdf_est, slope = FALSE)

# Also similar results for pand and randomization tests:
pand(scdf)
pand(scdf_est)
rand_test(scdf)
rand_test(scdf_est)

Description

Export creates html files of tables or displays them directly in the viewer pane of rstudio. When applied in rmarkdown/quarto, tables can also be created for pdf/latex output.

Usage

export(object, ...)

## S3 method for class 'sc_desc'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  flip = FALSE,
  round = 2,
  ...
)

## S3 method for class 'sc_nap'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  select = c("Case", "NAP", "NAP Rescaled", "w", "p", "d", "R²"),
  round = 2,
  ...
)

## S3 method for class 'sc_overlap'
export(
  object,
  caption = NA,
  footnote = NULL,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  round = 2,
  decimals = 2,
  flip = FALSE,
  ...
)

## S3 method for class 'sc_pem'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  round = 2,
  ...
)

## S3 method for class 'sc_pet'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  round = 1,
  ...
)

## S3 method for class 'sc_pnd'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  select = c("Case", "PND", "Total", "Exceeds"),
  round = 2,
  ...
)

## S3 method for class 'sc_power'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  round = 3,
  ...
)

## S3 method for class 'sc_smd'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  select = c("Case", `Mean A` = "mA", `Mean B` = "mB", `SD A` = "sdA", `SD B` = "sdB",
    `SD Cohen` = "sd cohen", `SD Hedges` = "sd hedges", "Glass' delta", "Hedges' g",
    "Hedges' g correction", "Hedges' g durlak correction", "Cohen's d"),
  kable_styling_options = list(),
  kable_options = list(),
  round = 2,
  decimals = 2,
  flip = FALSE,
  ...
)

## S3 method for class 'sc_trend'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  round = 2,
  decimals = 2,
  ...
)

## S3 method for class 'scdf'
export(
  object,
  summary = FALSE,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  cols,
  round = 2,
  ...
)

## S3 method for class 'scdf_summary'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  round = 2,
  ...
)

## S3 method for class 'sc_plm'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  nice = TRUE,
  ci = 0.95,
  q = FALSE,
  ...
)

Arguments

Value

Returns or displays a specially formatted html (or latex) file.

Description

The fillmissing() function replaces missing measurements in single-case data.

Usage

fill_missing(data, dvar, mvar, na.rm = TRUE)

Arguments

Details

This procedure is recommended if there are gaps between measurement times (e.g. MT: 1, 2, 3, 4, 5, ... 8, 9) or explicitly missing values in your single-case data and you want to calculate overlap indices (overlap()) or a randomization test (rand_test()).

Value

A single-case data frame with interpolated missing data points. See scdf() to learn about the SCDF Format.

Author(s)

Juergen Wilbert

See Also

Other data manipulation functions: add_l2(), as.data.frame.scdf(), as_scdf(), moving_median(), outlier(), ranks(), rescale(), scdf(), select_cases(), set_vars(), shift(), smooth_cases(), standardize(), truncate_phase()

Examples

## In his study, Grosche (2011) could not realize measurements each
## single week for all participants. During the course of 100 weeks,
## about 20 measurements per person at different times were administered.

## Fill missing values in a single-case dataset with discontinuous
## measurement times
Grosche2011filled <- fill_missing(Grosche2011)
study <- c(Grosche2011[2], Grosche2011filled[2])
names(study) <- c("Original", "Filled")
plot(study)

## Fill missing values in a single-case dataset that are NA
Maggie <- random_scdf(design(level = list(0,1)), seed = 123)
Maggie_n <- Maggie
replace.positions <- c(10,16,18)
Maggie_n[[1]][replace.positions,"values"] <- NA
Maggie_f <- fill_missing(Maggie_n)
study <- c(Maggie, Maggie_n, Maggie_f)
names(study) <- c("original", "missing", "interpolated")
plot(study, marks = list(positions = replace.positions), style = "grid2")

Description

The hplm() function computes a hierarchical piecewise regression model.

Usage

hplm(
  data,
  dvar,
  pvar,
  mvar,
  model = c("W", "H-M", "B&L-B", "JW"),
  contrast = c("first", "preceding"),
  contrast_level = NA,
  contrast_slope = NA,
  method = c("ML", "REML"),
  control = list(opt = "optim"),
  random.slopes = FALSE,
  lr.test = FALSE,
  ICC = TRUE,
  trend = TRUE,
  level = TRUE,
  slope = TRUE,
  random_trend = FALSE,
  random_level = FALSE,
  random_slope = FALSE,
  fixed = NULL,
  random = NULL,
  update.fixed = NULL,
  data.l2 = NULL,
  ...
)

## S3 method for class 'sc_hplm'
print(x, digits = 3, smd = FALSE, casewise = FALSE, ...)

## S3 method for class 'sc_hplm'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  round = 2,
  nice = TRUE,
  casewise = FALSE,
  ...
)

## S3 method for class 'sc_hplm'
coef(object, casewise = FALSE, ...)

Arguments

Value

Functions

• print(sc_hplm): Print results

• export(sc_hplm): Export results as html table (see export())

• coef(sc_hplm): Extract model coefficients

Author(s)

Juergen Wilbert

See Also

Other regression functions: autocorr(), bplm(), corrected_tau(), mplm(), plm(), trend()

Examples

## Compute hplm model on a MBD over fifty cases (restricted log-likelihood)
hplm(exampleAB_50, method = "REML", random.slopes = FALSE)

## Analyzing with additional L2 variables
Leidig2018 |>
  add_l2(Leidig2018_l2) |>
  hplm(update.fixed = .~. + gender + migration + ITRF_TOTAL*phaseB,
       slope = FALSE, random.slopes = FALSE, lr.test = FALSE
  )

Description

ird() calculates the robust improvement rate difference as proposed by Parker and colleagues (2011).

Usage

ird(data, dvar, pvar, decreasing = FALSE, phases = c(1, 2))

## S3 method for class 'sc_ird'
print(x, digits = 3, ...)

Arguments

Details

The adaptation of the improvement rate difference for single-case phase comparisons was developed by Parker and colleagues (2009). A variation called robust improvement rate difference was proposed by Parker and colleagues in 2011. This function calculates the robust improvement rate difference. It follows the formula suggested by Pustejovsky (2019). For a multiple case design, ird is based on the overall improvement rate of all cases which is the average of the irds for each case.

Functions

• print(sc_ird): Print results

References

Parker, R. I., Vannest, K. J., & Brown, L. (2009). The improvement rate difference for single-case research. Exceptional Children, 75(2), 135-150.

Parker, R. I., Vannest, K. J., & Davis, J. L. (2011). Effect Size in Single-Case Research: A Review of Nine Nonoverlap Techniques. Behavior Modification, 35(4), 303-322. https://doi.org/10.1177/0145445511399147

Pustejovsky, J. E. (2019). Procedural sensitivities of effect sizes for single-case designs with directly observed behavioral outcome measures. Psychological Methods, 24(2), 217-235. https://doi.org/10.1037/met0000179

See Also

Other overlap functions: cdc(), nap(), overlap(), pand(), pem(), pet(), pnd(), tau_u()

Description

scdf objects Tests for objects of type "scdf"

Usage

is.scdf(x)

Arguments

Value

Returns TRUE or FALSE depending on whether its argument is of scdf type or not.

Description

Takes an scdf and applies transformations to each individual case. This is useful to calculate or modify new variables.

Usage

moving_median(x, lag = 1)

moving_mean(x, lag = 1)

local_regression(x, mt = 1:length(x), f = 0.2)

first_of(x, positions = 0)

across_cases(...)

all_cases(...)

## S3 method for class 'scdf'
transform(`_data`, ...)

Arguments

Details

This function is a method of the generic transform function. Unlike the generic function, it calculates expressions serially. This means that the results of the calculation of one expression are the basis for the following computations. The n function returns the number of measurements in a case. The all_cases function is a helper function that extracts the values of a variable from all cases. It takes an expression as an argument. For example, mean(all_cases(values)) calculates the mean of the values from all cases. mean(all_cases(values[phase == "A"])) will calculate the mean of all values where phase is A. The function across_cases allows to calculate new variables or replace existing variables across all cases. E.g., across_cases(values_ranked = rank(values, na.last = "keep")) will calculate a new variable with values ranked across all cases.

Value

An scdf.

See Also

Other data manipulation functions: add_l2(), as.data.frame.scdf(), as_scdf(), fill_missing(), outlier(), ranks(), rescale(), scdf(), select_cases(), set_vars(), shift(), smooth_cases(), standardize(), truncate_phase()

Examples

## Creates a single-case with frequency distributions. The proportion and
## percentage of the frequencies are calculated with transform:
design <- design(
 n = 3,
 level = 5,
 distribution = "binomial",
 n_trials = 20,
 start_value = 0.5
)
study <- random_scdf(design)
transform(study, proportion = values/trials, percentage = proportion * 100)

## Z standardize the dependent variable and add two new variables:
exampleAB |>
  transform(
    values = scale(values),
    mean_values = mean(values),
    sd_values = sd(values)
  )

## Use `all` to calculate global variables.
exampleAB |>
  transform(
    values_center_case = values - mean(values[phase == "A"]),
    values_center_global = values - mean(all(values[phase == "A"])),
    value_dif = values_center_case - values_center_global
  )

## Use `across_cases` to calculate or replace a variable with values from
## all cases. E.g., standardize the dependent variable:
exampleABC |>
  transform(
    across_cases(values = scale(values))
  )

## Rank transform the values based on all cases vs. within each case:
exampleABC |>
  transform(
    across_cases(values_across = rank(values, na.last="keep")),
    value_within = rank(values, na.last="keep")
  )

## Three helper functions to smooth the data
Huber2014$Berta |>
transform(
  "compliance (moving median)" = moving_median(compliance),
  "compliance (moving mean)" = moving_mean(compliance),
  "compliance (local regression)" = local_regression(compliance, mt)
)

## Function first_of() helps to set NAs for specific phases.
## E.g., you want to replace the first two values of phase A and the first
## value of phase B and its preceding value.

byHeart2011 |>
  transform(
    values = replace(values, first_of(phase == "A", 0:1), NA),
    values = replace(values, first_of(phase == "B", -1:0), NA)
  )

Description

The mplm() function computes a multivariate piecewise regression model.

Usage

mplm(
  data,
  dvar,
  mvar,
  pvar,
  model = c("W", "H-M", "B&L-B", "JW"),
  contrast = c("first", "preceding"),
  contrast_level = c(NA, "first", "preceding"),
  contrast_slope = c(NA, "first", "preceding"),
  trend = TRUE,
  level = TRUE,
  slope = TRUE,
  formula = NULL,
  update = NULL,
  na.action = na.omit,
  ...
)

## S3 method for class 'sc_mplm'
print(x, digits = "auto", std = FALSE, ...)

Arguments

Value

Functions

• print(sc_mplm): Print results

Author(s)

Juergen Wilbert

See Also

Other regression functions: autocorr(), bplm(), corrected_tau(), hplm(), plm(), trend()

Examples

res <- mplm(Leidig2018$`1a1`,
  dvar = c("academic_engagement", "disruptive_behavior")
)
print(res)
## also report standardized coefficients:
print(res, std = TRUE)

Description

scdf objects Removes any row with a missing value

Usage

## S3 method for class 'scdf'
na.omit(object, ...)

Arguments

Value

A scdf object.

Description

The nap() function calculates the nonoverlap of all pairs (NAP; Parker & Vannest, 2009). NAP summarizes the overlap between all pairs of phase A and phase B data points. If an increase of phase B scores is expected, a non-overlapping pair has a higher phase B data point. The NAP equals number of pairs showing no overlap / number of pairs where ties are counted as half non-overlaps. Because NAP can take values between 0 and 100 percent where values below 50 percent indicate an inverse effect, an nap rescaled from -100 to 100 percent where negative values indicate an inverse effect is also displayed ($nap_{rescaled} = 2 * nap - 100$).

Usage

nap(data, dvar, pvar, decreasing = FALSE, phases = c(1, 2))

Arguments

Value

Author(s)

Juergen Wilbert

References

Parker, R. I., & Vannest, K. (2009). An improved effect size for single-case research: Nonoverlap of all pairs. Behavior Therapy, 40, 357-367.

See Also

Other overlap functions: cdc(), ird(), overlap(), pand(), pem(), pet(), pnd(), tau_u()

Examples

## Calculate NAP for a study with  lower expected phase B scores
## (e.g. aggressive behavior)
gretchen <- scdf(c(A = 12, 14, 9, 10, B = 10, 6, 4, 5, 3, 4))
nap(gretchen, decreasing = TRUE)

## Request NAP for all cases from the Grosche2011 scdf
nap(Grosche2011)

Description

Identifies and drops outliers within a single-case data frame (scdf).

Usage

outlier(
  data,
  dvar,
  pvar,
  mvar,
  method = c("MAD", "Cook", "SD", "CI"),
  criteria = 3.5
)

Arguments

Details

For method = "SD", criteria = 2 would refer t0 two standard deviations. For method = "MAD", criteria = 3.5 would refer to 3.5 times the mean average deviation. For method = "CI", criteria = 0.99 would refer to a 99 percent confidence interval. For method = "cook", criteria = "4/n" would refer to a Cook's Distance greater than 4/n.

Value

Author(s)

Juergen Wilbert

See Also

Other data manipulation functions: add_l2(), as.data.frame.scdf(), as_scdf(), fill_missing(), moving_median(), ranks(), rescale(), scdf(), select_cases(), set_vars(), shift(), smooth_cases(), standardize(), truncate_phase()

Examples

## Identify outliers using 1.5 standard deviations as criterion
susanne <- random_scdf(level = 1.0)
res_outlier <- outlier(susanne, method = "SD", criteria = 1.5)
res_outlier

## Identify outliers in the original data from Grosche (2011)
## using Cook's Distance greater than 4/n as criterion
res_outlier <- outlier(Grosche2011, method = "Cook", criteria = "4/n")
res_outlier

Description

The overlap function provides the most common overlap indices for single-case data and some additional statistics.

Usage

overlap(data, dvar, pvar, mvar, decreasing = FALSE, phases = c(1, 2))

Arguments

Details

See corresponding functions of PND, PEM, PET, NAP, PAND for calculation. Tau_U(A) reports "A vs. B - Trend A" whereas Tau_U(BA) reports "A vs. B + Trend B - Trend A". Base_Tau is baseline corrected tau (correction applied when autocorrelation in phase A is significant). Diff_mean is the mean difference. Diff_trend is the difference in the regression estimation of the dependent variable on measurement-time (x ~ mt) for each phase. SMD is the mean difference divided by the standard deviation of phase A. Hedges_g is the mean difference divided by the pooled standard deviation: $\sqrt{ (n_A - 1)sd_A^2 + (n_B - 1)sd_B^2 \over n_A + n_B - 2 }$ with a hedges correction applied: $Hedges_g * (1 - \frac{3}{4n - 9} ) )$.

Value

Author(s)

Juergen Wilbert

See Also

Other overlap functions: cdc(), ird(), nap(), pand(), pem(), pet(), pnd(), tau_u()

Examples

## Display overlap indices for one single-case
overlap(Huitema2000, decreasing = TRUE)

## Display overlap indices for six single-cases
overlap(GruenkeWilbert2014)

## Combining phases for analyszing designs with more than two phases
overlap(exampleA1B1A2B2, phases = list(c("A1","A2"), c("B1","B2")))

Description

The pand() function calculates the percentage of all non-overlapping data (PAND; Parker, Hagan-Burke, & Vannest, 2007), an index to quantify a level increase (or decrease) in performance after the onset of an intervention.

Usage

pand(
  data,
  dvar,
  pvar,
  decreasing = FALSE,
  phases = c(1, 2),
  method = c("sort", "minimum")
)

## S3 method for class 'sc_pand'
print(x, ...)

## S3 method for class 'sc_pand'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  round = 1,
  ...
)

Arguments

Details

PAND was proposed by Parker, Hagan-Burke, and Vannest in 2007. The authors emphasize that PAND is designed for application in a multiple case design with a substantial number of measurements, technically at least 20 to 25, but preferably 60 or more. PAND is defined as 100% minus the percentage of data points that need to be removed from either phase in order to ensure nonoverlap between the phases. Several approaches have been suggested to calculate PAND, leading to potentially different outcomes. In their 2007 paper, Parker and colleagues present an algorithm for computing PAND. The algorithm involves sorting the scores of a time series, including the associated phases, and comparing the resulting phase order with the original phase order using a contingency table. To account for ties, the algorithm includes a randomization process where ties are randomly assigned to one of the two phases. Consequently, executing the algorithm multiple times could yield different results. It is important to note that this algorithm does not produce the same results as the PAND definition provided earlier in the same paper. However, it offers the advantage of allowing the calculation of an effect size measure phi, and the application of statistical tests for frequency distributions. Pustejovsky (2019) presented a mathematical formulation of Parker's original definition for comparing two phases of a single case:

$PAND = \frac{1}{m+n}max\{(i+j)I(y^A_{i}<y^B_{n+1-j}\}$

This formulation provides accurate results for PAND, but the original definition has the drawback of an unknown distribution under the null hypothesis, making a statistical test difficult. The pand() function enables the calculation of PAND using both methods. The first approach (method = "sort") follows the algorithm described above, with the exclusion of randomization before sorting to avoid ambiguity. It calculates a phi measure and provides the results of a chi-squared test and a Fisher exact test. The second approach (method = "minimum") applies the aforementioned formula. The code of this function is based on the code of the SingleCaseES package (function calc_PAND). For a multiple case design, overlaps are calculated for each case, summed, and then divided by the total number of measurements. No statistical test is conducted for this method.

Value

Functions

• print(sc_pand): Print results

• export(sc_pand): Export results as html table (see export())

Author(s)

Juergen Wilbert

References

Parker, R. I., Hagan-Burke, S., & Vannest, K. (2007). Percentage of All Non-Overlapping Data (PAND): An Alternative to PND. The Journal of Special Education, 40, 194-204.

Parker, R. I., & Vannest, K. (2009). An Improved Effect Size for Single-Case Research: Nonoverlap of All Pairs. Behavior Therapy, 40, 357-367.

Pustejovsky, J. E. (2019). Procedural sensitivities of effect sizes for single-case designs with directly observed behavioral outcome measures. Psychological Methods, 24(2), 217-235. https://doi.org/10.1037/met0000179

Pustejovsky JE, Chen M, Swan DM (2023). SingleCaseES: A Calculator for Single-Case Effect Sizes. R package version 0.7.1.9999, https://jepusto.github.io/SingleCaseES/.

See Also

Other overlap functions: cdc(), ird(), nap(), overlap(), pem(), pet(), pnd(), tau_u()

Examples

## REplication of the Parker et al. 2007 example
pand(Parker2007)

## Calculate the PAND with an expected decrease of phase B scores
cubs <- scdf(c(20,22,24,17,21,13,10,9,20,9,18), B_start = 5)
pand(cubs, decreasing = TRUE)

Description

The pem function returns the percentage of phase B data exceeding the phase A median. Additionally, a chi square test against a 50/50 distribution is computed. Different measures of central tendency can be addressed for alternative analyses.

Usage

pem(
  data,
  dvar,
  pvar,
  decreasing = FALSE,
  binom.test = TRUE,
  chi.test = FALSE,
  FUN = median,
  phases = c(1, 2),
  ...
)

Arguments

Author(s)

Juergen Wilbert

See Also

Other overlap functions: cdc(), ird(), nap(), overlap(), pand(), pet(), pnd(), tau_u()

Examples

## Calculate the PEM including the Binomial and Chi-square tests for a single-case
dat <- random_scdf(5, level = 0.5)
pem(dat, chi.test = TRUE)

Description

The pet function returns the percentage of Phase B data points that exceed the prediction based on the Phase A trend. A binomial test against a 50/50 distribution is calculated. It also calculates the percentage of Phase B data points that exceed the upper (or lower) 95 percent confidence interval of the predicted progression.

Usage

pet(data, dvar, pvar, mvar, ci = 0.95, decreasing = FALSE, phases = c(1, 2))

Arguments

Value

Author(s)

Juergen Wilbert

See Also

Other overlap functions: cdc(), ird(), nap(), overlap(), pand(), pem(), pnd(), tau_u()

Examples

## Calculate the PET and use a 99%-CI for the additional calculation
# create random example data
design <- design(n = 5, slope = 0.2)
dat <- random_scdf(design, seed = 23)
pet(dat, ci = .99)

Description

The plm function computes a piecewise regression model (see Huitema & McKean, 2000).

Usage

plm(
  data,
  dvar,
  pvar,
  mvar,
  AR = 0,
  model = c("W", "H-M", "B&L-B", "JW"),
  family = "gaussian",
  trend = TRUE,
  level = TRUE,
  slope = TRUE,
  contrast = c("first", "preceding"),
  contrast_level = c(NA, "first", "preceding"),
  contrast_slope = c(NA, "first", "preceding"),
  formula = NULL,
  update = NULL,
  na.action = na.omit,
  r_squared = TRUE,
  var_trials = NULL,
  dvar_percentage = FALSE,
  ...
)

Arguments

Value

Author(s)

Juergen Wilbert

References

Beretvas, S., & Chung, H. (2008). An evaluation of modified R2-change effect size indices for single-subject experimental designs. Evidence-Based Communication Assessment and Intervention, 2, 120-128.

Huitema, B. E., & McKean, J. W. (2000). Design specification issues in time-series intervention models. Educational and Psychological Measurement, 60, 38-58.

See Also

Other regression functions: autocorr(), bplm(), corrected_tau(), hplm(), mplm(), trend()

Examples

## Compute a piecewise regression model for a random single-case
set.seed(123)
AB <- design(
  phase_design = list(A = 10, B = 20),
  level = list(A = 0, B = 1), slope = list(A = 0, B = 0.05),
  trend = 0.05
)
dat <- random_scdf(design = AB)
plm(dat, AR = 3)

## Another example with a more complex design
A1B1A2B2 <- design(
  phase_design = list(A1 = 15, B1 = 20, A2 = 15, B2 = 20),
  level = list(A1 = 0, B1 = 1, A2 = -1, B2 = 1),
  slope = list(A1 = 0, B1 = 0.0, A2 = 0, B2 = 0.0),
  trend = 0.0)
dat <- random_scdf(design = A1B1A2B2, seed = 123)
plm(dat, contrast = "preceding")

## no slope effects were found. Therefore, you might want to the drop slope
## estimation:
plm(dat, slope = FALSE, contrast = "preceding")

## and now drop the trend estimation as well
plm(dat, slope = FALSE, trend = FALSE, contrast = "preceding")

## A poisson regression
example_A24 |>
  plm(family = "poisson")

## A binomial regression (frequencies as dependent variable)
plm(exampleAB_score$Christiano, family = "binomial", var_trials = "trials")

## A binomial regression (percentage as dependent variable)
exampleAB_score$Christiano |>
  transform(percentage = values/trials) |>
  set_dvar("percentage") |>
  plm(family = "binomial", var_trials = "trials", dvar_percentage = TRUE)

Description

This function takes the return of the rand_test function and creates a histogram with the distribution of the rand sample statistics.

Usage

plot_rand(
  object,
  type = "hist",
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  text_observed = "observed",
  color = "lightgrey",
  ...
)

Arguments

Description

This function provides a plot of a single-case or multiple single-cases.

Usage

## S3 method for class 'scdf'
plot(...)

plotSC(
  data,
  dvar,
  pvar,
  mvar,
  ylim = NULL,
  xlim = NULL,
  xinc = 1,
  lines = NULL,
  marks = NULL,
  phase.names = NULL,
  xlab = NULL,
  ylab = NULL,
  main = "",
  case.names = NULL,
  style = getOption("scan.plot.style"),
  ...
)

Arguments

Value

Returns a plot of one or multiple single-cases.

Author(s)

Juergen Wilbert

Examples

## Request the default plot of the data from Borckhardt (2014)
plot(Borckardt2014)

## Plot the three cases from Grosche (2011) and visualize the phase A trend
plot(Grosche2011, style = "grid", lines = "trendA")

## Request the local regression line for Georg from that data set and customize the plot
plot(Grosche2011$Georg, style = "sienna", ylim = c(0,NA),
       xlab = "Training session", ylab = "Words per minute",
       phase.names = c("Baseline", "Intervention"), xinc = 5,
       lines = list(type = "loreg", f = 0.2, lty = "solid", col = "black", lwd = 3))

## Plot a random MBD over three cases and mark interesting MTs
dat <- random_scdf(design = design(3))
plot(dat, marks = list(positions = list(c(2,4,5),c(1,2,3),c(7,8,9)), col = "blue",
       cex = 1.4), style = c("grid", "annotate", "tiny"))

Description

This function returns the percentage of non-overlapping data. Due to its error-proneness the PND should not be used, but nap or pand instead (see Parker & Vannest, 2009).

Usage

pnd(data, dvar, pvar, decreasing = FALSE, phases = c(1, 2))

Arguments

Value

Author(s)

Juergen Wilbert

See Also

Other overlap functions: cdc(), ird(), nap(), overlap(), pand(), pem(), pet(), tau_u()

Examples

## Calculate the PND for multiple single-case data
pnd(GruenkeWilbert2014)

Description

Conducts a Monte-Carlo study on the test-power and alpha-error probability of a statistical function.

Usage

power_test(
  design,
  method = c("plm_level", "rand", "tauU"),
  effect = "level",
  n_sim = 100,
  design_is_one_study = TRUE,
  alpha_test = TRUE,
  power_test = TRUE,
  binom_test = FALSE,
  binom_test_alpha = FALSE,
  binom_test_power = FALSE,
  binom_test_correct = FALSE,
  ci = FALSE,
  alpha_level = 0.05
)

Arguments

Details

Based on a design() object, a large number of single-cases are generated and re-analyzed with a provided statistical function. The proportion of significant analyzes is the test power. In a second step, a specified effect of the design object is set to 0 and again single-cases are generated and reanalyzed. The proportion of significant analyzes is the alpha error probability.

Author(s)

Juergen Wilbert

See Also

random_scdf(), design()

Examples

## Assume you want to conduct a single-case study with 15 measurements
## (phases: A = 6 and B = 9) using a highly reliable test and
## an expected level effect of d = 1.4.
## A (strong) trend effect is trend = 0.05. What is the power?
## (Note: n_sims is set to 10. Set n_sims to 1000 for a serious calculation.)
design <- design(
  n = 1, phase_design = list(A = 6, B = 9),
  rtt = 0.8, level = 1.4, trend = 0.05
)
power_test(design, n_sim = 10)

## Would you achieve higher power by setting up a MBD with three cases?
design <- design(
  n = 3, phase_design = list(A = 6, B = 9),
  rtt = 0.8, level = 1.4, trend = 0.05
)
power_test(design, n_sim=10, method=list("hplm_level", "rand", "tauU_meta"))

Description

Print an scdf

Usage

## S3 method for class 'scdf'
print(
  x,
  cases = getOption("scan.print.cases"),
  rows = getOption("scan.print.rows"),
  cols = getOption("scan.print.cols"),
  long = getOption("scan.print.long"),
  digits = getOption("scan.print.digits"),
  ...
)

Arguments

Details

Print options for scdf objects could be set globally: option(scan.print.cases = "all"), option(scan.print.rows = 10), option(scan.print.cols = "main"), option(scan.print.long = TRUE), option(scan.print.digits = 0), option(scan.print.scdf.name = FALSE)

Description

The rand_test function computes a randomization test for single or multiple baseline single-case data. The function is based on an algorithm from the SCRT package (Bulte & Onghena, 2009, 2012), but rewritten and extended for the use in AB designs.

Usage

rand_test(
  data,
  dvar,
  pvar,
  statistic = c("Mean B-A", "Mean A-B", "Median B-A", "Median A-B", "Mean |A-B|",
    "Median |A-B|", "SMD hedges", "SMD glass", "W-test", "T-test", "NAP",
    "NAP decreasing", "Slope B-A", "Slope A-B"),
  statistic_function = NULL,
  number = 500,
  complete = FALSE,
  limit = 5,
  startpoints = NA,
  exclude.equal = FALSE,
  phases = c(1, 2),
  graph = FALSE,
  output = NULL,
  seed = NULL
)

Arguments

Value

Details

Predefinded statisic

Use the statistic argument to choose a predefnied statistic. The following comparisons are possible:

• ⁠Mean A-B⁠: Uses the difference between the mean of phase A and the mean of phase B. This is appropriate if a decrease of scores was expected for phase B.

• ⁠Mean B-A⁠: Uses the difference between the mean of phase B and the mean of phase A. This is appropriate if an increase of scores was expected for phase B.

• ⁠Mean |A-B|⁠: Uses the absolute value of the difference between the means of phases A and B.

• ⁠Median A-B⁠: The same as ⁠Mean A-B⁠, but based on the median.

• ⁠Median B-A⁠: The same as ⁠Mean B-A⁠, but based on the median.

• ⁠SMD hedges / SMD glass⁠: Standardizes mean difference of B-A as Hedges's g or Glass' delta.

• NAP: Non-overlap of all pairs.

• W-test: Wilcoxon-test statistic W.

• T-test: T-test statistic t.

Create own statistic function

Use the statistic_function argument to proved your own function in a list. This list must have an element named statistic with a function that takes two arguments a and b and returns a single numeric value. E.g. ⁠list(statistic = function(a, b) mean(a) - mean(b)⁠. A second element of the list is named aggregate which takes a function with one numeric argument that returns a numeric argument. This function is used to aggregate the values of a multiple case design. If you do not provide this element, it uses the default function(x) sum(x)/length(x). The third optional argument is name which provides a name for your user function.

Author(s)

Juergen Wilbert

References

Bulte, I., & Onghena, P. (2009). Randomization tests for multiple-baseline designs: An extension of the SCRT-R package. Behavior Research Methods, 41, 477-485.

Bulte, I., & Onghena, P. (2012). SCRT: Single-Case Randomization Tests. Available from: https://CRAN.R-project.org/package=SCRT

Examples

## Compute a randomization test on the first case of the byHeart2011 data and include a graph
rand_test(byHeart2011[1], statistic = "Median B-A", graph = TRUE, seed = 123)

## Compute a randomization test on the Grosche2011 data using complete permutation
rand_test(Grosche2011, statistic = "Median B-A", complete = TRUE, limit = 4, seed = 123)

Description

The random_scdf function generates random single-case data frames for monte-carlo studies and demonstration purposes. design is used to set up a design matrix with all parameters needed for the random_scdf function.

Usage

random_scdf(design = NULL, round = NA, random_names = FALSE, seed = NULL, ...)

Arguments

Value

A single-case data frame. See scdf to learn about this format.

Author(s)

Juergen Wibert

Examples

## Create random single-case data and inspect it
design <- design(
  n = 3, rtt = 0.75, slope = 0.1, extreme_prop = 0.1,
  missing_prop = 0.1
)
dat <- random_scdf(design, round = 1, random_names = TRUE, seed = 123)
describe(dat)

## And now have a look at poisson-distributed data
design <- design(
  n = 3, B_start = c(6, 10, 14), mt = c(12, 20, 22), start_value = 10,
  distribution = "poisson", level = -5, missing_prop = 0.1
)
dat <- random_scdf(design, seed = 1234)
pand(dat, decreasing = TRUE)

Description

The rci() function computes indices of reliable change (Wise, 2004) and corresponding descriptive statistics.

Usage

rci(data, dvar, pvar, rel, ci = 0.95, graph = FALSE, phases = c(1, 2))

Arguments

Author(s)

Juergen Wilbert

References

Christensen, L., & Mendoza, J. L. (1986). A method of assessing change in a single subject: An alteration of the RC index. Behavior Therapy, 17, 305-308.

Jacobson, N. S., & Truax, P. (1991). Clinical Significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59, 12-19.

Wise, E. A. (2004). Methods for analyzing psychotherapy outcomes: A review of clinical significance, reliable change, and recommendations for future directions. Journal of Personality Assessment, 82, 50 - 59.

Examples

## Report the RCIs of the first case from the byHeart data and include a graph
rci(byHeart2011[1], graph = TRUE, rel = 0.8)

Description

Use the read_scdf function to load single-case data csv, excel, or yaml files.

Usage

read_scdf(
  file,
  cvar = "case",
  pvar = "phase",
  dvar = "values",
  mvar = "mt",
  sort_cases = FALSE,
  phase_names = NULL,
  type = NA,
  na = c("", "NA"),
  sort.labels = NULL,
  phase.names = NULL,
  ...
)

Arguments

Value

Returns a single-case data frame. See scdf to learn about the format of these data frames.

Author(s)

Juergen Wilbert

See Also

read.table(), readRDS()

Other io-functions: convert(), write_scdf()

Examples

## Read SC-data from a file named "study1.csv" in your working directory
# study1 <- read_scdf("study1.csv")

## Read SC-data from a .csv-file with semicolon as field and comma as decimal separator
# study2 <- read_scdf("study2.csv", sep = ";", dec = ",")

## write_scdf and read_scdf
filename <- file.path(tempdir(), "test.csv")
write_scdf(exampleA1B1A2B2_zvt, filename)
dat <- read_scdf(filename, cvar = "case", pvar = "part", dvar = "zvt", mvar = "day")
res1 <- describe(exampleA1B1A2B2_zvt)$descriptives
res2 <- describe(dat)$descriptives
all.equal(res1,res2)

Description

Samples random names

Usage

sample_names(n = 1, type = "neutral", seed = NULL)

Arguments

Value

A character vector with random names

Examples

sample_names(3)

Description

scdf() is the constructor for the scdf class. It stores single-case study data with one or more single-cases.

Usage

scdf(
  values,
  B_start,
  mt,
  phase,
  phase_design = NULL,
  phase_starts = NULL,
  name = NULL,
  dvar = "values",
  pvar = "phase",
  mvar = "mt",
  ...
)

Arguments

Details

If the dependent variable is a named vector then the names are extracted to create a phase design (e.g., values = c(A = 2,3,5,4,3, B = 6,5,4,3) will create an AB phase design with five and four measurements). An scdf contains several attributes: dvar The name of the dependent variable. phase The name of the phase variable. mt The name of the measurement time variable. author Information on the author of the data. info Further information on the data. E.g., a publication. ⁠dvar, phase⁠, and mt are the defaults most of the scan function use. You can change the values of the attributes with the scdf_attr function (e.g., scdf_attr(exampleAB_add, "dvar") <- "depression" defines depression as the dependent variable. Please notice that all scan functions have arguments to define dvar, phase, and mt for a given analysis.

Value

Returns a single-case data frame scdf suitable for all functions of the scan package. Multiple data sets (e.g. from Multiple Baseline Designs) can be listed.

Author(s)

Juergen Wilbert

See Also

Other data manipulation functions: add_l2(), as.data.frame.scdf(), as_scdf(), fill_missing(), moving_median(), outlier(), ranks(), rescale(), select_cases(), set_vars(), shift(), smooth_cases(), standardize(), truncate_phase()

Examples

## Scores on a letter naming task were collected on eleven days in a row.
## The intervention started after the fifth measurement,
## so the first B phase measurement was 6 (B_start = 6).
klaas <- scdf(
  c(5, 7, 8, 5, 7, 12, 16, 18, 15, 14, 19),
  B_start = 6, name = "Klaas"
)
describe(klaas)

# Alternative coding 1:
klaas <- scdf(
  c(A = 5, 7, 8, 5, 7, B = 12, 16, 18, 15, 14, 19),
  name = "Klaas"
)

# Alternative coding 2:
klaas <- scdf(
  c(5, 7, 8, 5, 7, 12, 16, 18, 15, 14, 19),
  phase_design = c(A = 5, B = 6), name = "Klaas"
)

# Alternative coding 3:
klaas <- scdf(
  c(5, 7, 8, 5, 7, 12, 16, 18, 15, 14, 19),
  phase_starts = c(A = 1, B = 7), name = "Klaas"
)

## Unfortunately in a similar study there were no data collected on
## days 3 and 9. Use NA to pass them to the function:
emmi <- scdf(c(5, 7, NA, 5, 7, 12, 16, 18, NA, 14, 19),
  phase_design = c(A = 5, B = 6), name = "Emmi"
)
describe(emmi)

## In a MBD over three cases, data were collected eleven days in a row.
## Intervention starting points differ between subjects as they were
## randomly assigned. The three SCDFs are then combined in a list for
## further conjoined analyses.
charlotte <- scdf(c(A = 5, 7, 10, 5, 12, B = 7, 10, 18, 15, 14, 19))
theresa <- scdf(c(A = 3, 4, 3, 5, B = 7, 4, 7, 9, 8, 10, 12))
antonia <- scdf(c(A = 9, 8, 8, 7, 5, 7, B = 6, 14, 15, 12, 16))
mbd <- c(charlotte, theresa, antonia)
names(mbd) <- c("Charlotte", "Theresa", "Antonia")
overlap(mbd)

## In a classroom-based intervention it was not possible to measure outcomes
## every day, but only on schooldays. The sequence of measurements is passed
## to the package by using a vector of measurement times.
frida <- scdf(
  c(A = 3, 2, 4, 2, 2, 3, 5, 6, B = 8, 10, 8, 12, 14, 13, 12),
  mt = c(1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18)
)
summary(frida)
describe(frida)

## example with two independent variables and four phases
jim <- scdf(
  zvt = c(47, 58, 76, 63, 71, 59, 64, 69, 72, 77, 76, 73),
  d2 = c(131, 134, 141, 141, 140, 140, 138, 140, 141, 140, 138, 140),
  phase_design = c(A1 = 3, B1 = 3, A2 = 3, B2 = 3), dvar = "zvt"
)
overlap(jim, phases = list(c("A1", "A2"), c("B1", "B2")))