NEWS.md
In jazznbass/scan_develop: Single-Case Data Analyses for Single and Multiple Baseline Designs

scan 0.61.999

rescale(): New function as a helper for getting standardized estimators in regression models. e.g. exampleAB |> rescale() |> hplm().
between_smd(): Calculates between case standardized mean differences as proposed by Pustejovsky et. aL (2014). Can take complex hplm models as a basis.
print.sc_hplm(): New argument smd. If set TRUE, between case smd results are reported.
tau_u(): New method "tarlow" calculates Tau-U as implemented in an R code and online calculator by Tarlow (2017). Here, tau values are calculated as in the method = "complete", continuity_correction = TRUE, tau_method = "a". Inferential statistics are calculated based an tau b and the standard deviation for S is derived directly from Kendall's Tau B analysis (different from the parker and complete methods).
rand_test(): It is now possible to provide new functions for calculating the statistic directly with a list to the statistic_function argument. This list must have an element named statistic with a function that takes two arguments a and b and returns a single numeric value. 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.

userstat <- list(
  statistic = function(a, b) median(b) - median(a), 
  aggregate = function(x) median(x),
  name = "median B - A"
)

rand_test(exampleAB, statistic_function = userstat , complete = TRUE)

# which is identical to:
rand_test(exampleAB, statistic = "Median B-A" , complete = TRUE)

rand_test(): Returns startpoints for the random permutations.
plot_rand(): New argument type when "xy" a plot with splitpoints and statistics is drawn. This allows to see graphically at which measurement time a statistic changes.

Leidig2018[4] |> 
  na.omit() |> 
  rand_test(complete = TRUE, limit = 1, statistic = "SMD glass") |> 
  plot_rand(type = "xy")

na.omit.scdf(): scdf method for generic na.omit(). Removes any row with a missing value from an scdf.

scdf(): Throws an error when argument phase_starts is set and the beginning of the first phase is not the first measurement.
tau_u(): Method "parker" ignores the tau_method setting and sets continuity_correction = FALSE. This follows the Parker (2011) paper. There, the inferential statistics are calculated using Kendall's Tau b while the actual Tau calculation applies Kendall's Tau a (without ties).

scan 0.61.0

Corrected the name of the level-effect predictor for regressions when the phase variable is not named "phase".

Tip-of-the-day like message at start-up.
Multiple improvements of the Shiny app (try out with shinyscan())
new output engine for rendering html export based on gt table. Set options(scan.export.engine = "gt"). This engine allows to export tables into docx format: overlap(exampleAB) |> export(file = "test.docx", flip = TRUE).
new export functions for pem(), pet(), pnd(), and summary() (either summary(exampleAB) |> export() or export(exampleAB, summary = TRUE))

rci(): removed the Hageman et al. method as it is not appropriate for single-cases in the current implementation.
scdf(): New argument phase_starts(). Which defines the measurement times of the start of each phase. phase_starts = list(A = 1, B = 10, C = 15). It throws an error when a phase start is defined where no corresponding measurement-time exists. phase_starts is a generalization of B_start.
rand_test(): New option for statistic: SMD calculates the standardized mean difference as Hedge's g with Durlak correction. W-test computes Wilcoxon tests and compares average W statistics. T-test computes T-tests and compares average t-Values. NAP and NAP decreasing for Non-overlap of all pairs.
nap(): added Cohen's d and R-Squared effects.
export(): select argument for nap
coef.sc_hplm(): new casewise argument. If set TRUE, returns the effect estimations casewise.
print.sc_hplm(): new casewise argument. If set TRUE, returns the effect estimations casewise.
export.sc_hplm(): new casewise argument. If set TRUE, returns the effect estimations casewise.
export.scdf(): new summary argument. If TRUE, returns a summary.
hplm(): new arguments random_trend, random_level, and random_slope to selectively add respective random slope effects to the model.

Parker2009b

scan 0.60.0

ird(): Robust improvement rate difference as formulated by Postejovski (2019).

pand(): Rewrote function. New argument method allows to apply the sorting algorithm proposed in Parker 2007 and method = "minimum" applies the exact method provided by Pustejovski in 2019. Furthermore, the tau test was replaced with a X Squared and a Fisher exact test.
corrected_tau: Report results with warning when all phase A data are identical; new argument tau_method = "a" to switch to Kendall's tau-a.
export(): new for pand().
New example datasets: Tarlow2017, Parker2011b, Parker2009, Parker2007

Added power-analyses
Added settings
Extended save options
Various changes and optimization of the ui

scan 0.59.0

describe(): now works correctly for duplicated phase names.
shiny-app no longer depends on shinyjs and markdown. shinyscan() asks if missing packages scplot and shiny should be installed automatically.

scan 0.58

start the app with shinyscan()
you need the following packages to run the app:
shiny, shinyjs, scplot, markdown

batch_apply(): Apply a function to each element in an scdf. Use . as a placeholder for the scdf case.

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

Helper functions for transform(): n(), all_cases(), across_cases(), first_of
Helper functions for transform(): moving_mean(), moving_median(), local_regression()
corrected_tau(): returns multiple cases
export(): new for nap(), pen()
c()/combine(): new arguments to set author and info attributes of the resulting scdf (author, info)

plm(): Print function allows to set maximum lag for autocorrelations; Overall significance Ljung_Box test is reported.

plm(exampleAB$Johanna) |> print(lag_max = 5)

solved: summary() failed when one of the cases had no name
solved: nap() only reported values for the first case with multiple cases.
solved: add_l2() lost column name of l2 variable when l2 had only one variable.

convert(): new arguments. indent sets the indentation. When the scdf contains only one case, no study is combined.
select_phases(): New argument phase_names sets names of the recombined phases. The default "auto" creates combinations of the phase names automatically (e.g., A = c("A", "B", B = "C") results in phases AB and C).

scan 0.56

export(): New export for power_test() and smd() output.
export(): tau_u() export with new argument case which takes the values "meta" or "all" and new argument select allowing to select, reorder, and rename specific variables.
select_cases(): Allow for a selection based on object names (like in substitute).

select_cases(exampleAB, -c(Johanna, Karolina))
select_cases(exampleAB, Johanna, Karolina)
v <- c("Moritz", "Jannis")
select_cases(exampleA1B1A2B2, v)

plm(), hplm(): New arguments contrast_level and contrast_slope allow for setting the contrasts for level and slope separately. Both elements can either be "first" or "preceding".
Speed-up tau_u() by 20%.
rewrote as_scdf(), read_scdf(). Now it is easier to import data from any file format.

readODS::read_ods("filename.ods") |> as.scdf()
openxlsx::read.xlsx("filename.xlsx") |> as.scdf()


readODS::read_ods("filename.ods") |> 
  as.scdf(
    cvar = "id", 
    pvar = "section", 
    mvar = "day", 
    phase_names = c("baseline", "intervention")
  )

as.data.frame(exampleABC) |> readODS::write_ods("filename.xlsx")
as.data.frame(exampleABC) |> openxlsx::write.xlsx("filename.xlsx")

tau_u(): Rewrote the calculation of meta analyses and confidence intervals.
tau_u(), correted_tau(): corrected a wrong calculation of the continuity correction when values where lower in phase B.
tau_u(): Implemented a new method for calculating confidence intervals based on Fisher-Z transformations (see Long, J. D., & Cliff, N. (1997). Confidence intervals for Kendall’s tau. British Journal of Mathematical and Statistical Psychology, 50(1), 31-41.

smooth_caes(), shift(), standardise(), ranks(), truncate_phases(): All superseded by transform() and its helper functions. See details in the help files of transform and in the scan-book.

as.data.frane.scdf()/as_scdf(): keep and retrieve scdf attributes.

scan 0.55

coef()/ coefficients(): Method for base R coef function for plm/hplm objects. Extracts coefficient tables from provided object.

coef(plm(exampleAB$Johanna))

power_test(): New argument ci provides confidence intervals for power, alpha error, and correct proportions. New arguments binom_test_alpha, binom_test_power, binom_test_correct provide test against a provided proportion for alpha, power, and correct proportions.

design <- design(
  n = 1, phase_design = list(A = 6, B = 9),
  rtt = 0.8, level = 1.0, trend = 0.05
)

power_test(
  design, ci = 0.95, binom_test = TRUE
)

plm(), hplm(): Added contrast argument with values "first" or "preceding". Now model = "JW" is deprecated and identical to model = "B&L-B", contrast = "preceding".
plm(), hplm(): Added model "W". Which shifts the measurement-time variable to start with zero. This leads to a more sensible estimation of the intercept (where the intercept depicts the estimated score at the start). model = "W" is now the default. Use model = "B&L-B" for previous defaults.

solved #66: set_vars() working.
read_scdf() now works when cvar is not the first column.

corrected_tau(): changed default setting of arguments to: repeated = FALSE, continuity = FALSE. Now the default results match the calculator developed by Tarlow.
tau_u(): Implemented a continuity_correction (S-1 for calculating Z)

scan 0.54.1

select_phases() now works when phase variable is not "phase"
estimate_design() was broken and is fixed now.
print() function for sc_designfixed.

scan 0.54

transform(): Takes an scdf and calculates or modifies variables for each case (transform(exampleAB, z_values = scale(values), t_values = 50 + z_values * 10)).
smd() reporting various types of standardized mean differences.

readSC() -> read_scdf()
writeSC() -> write_scdf()
design_rSC() -> design()
rSC() -> random_scdf()

power_test() with various extensions, optimizations, and solved various bugs. rewrote the print method, added an argument duration to print the computation duration. Added the 'n_trials' argument for binomial distributions. Extended the help page.
design() and its print method. Extended the help page. Rewrote the algorithm for the 'binomial' distribution.

plm(): rewrote the analysis function for binomial tests. These now need an argument var_trials to define the number of trials per measurement. The dvar_percentage argument must be set TRUE when the dependent variables are percentages (and family = 'binomal').
speed optimized random_scdf(). Rewrote the algorithm for 'poisson' distributed measures. Rewrote the algorithm for the 'binomial' distribution. Extended the help page.
read_scdf(): extracts filetype from file extension.
read_scdf(): New yaml import options for scdf files

Anna:
  values:
    A: [1, 3, 4, 5, 6, 7]
    B: [8, 9, 10, 10, 11]

Toni:
  values:
    A: [2, 3, 4, 5, 6, 7]
    B: [3, 9, 10, 10,11]
  control_var: [1,2,3,4,5,6,7,8,1,2,3]

tau_u() #51: Added option for confidence intervals for tau_u output.

describe(): solved wrong calculation of Hedges G when phase length differed.
plm() solved #46: throws no error, when a phase is of length 1.
corrected_tau() solved #48: throws warning when A phase has less than three rows.
solved #49: changes class from tibble to data.frame within scdf.

scan 0.53

scdf files now allow to combine studies with different phase designs. Several functions have been adapted to handle cases with differing designs in a mutual analysis.
The %>% operator has been imported and exported from the magrittr package. Now that R 4.1 has a pipe operator, pipes seem to become the standard. For compatibility with older R Versions, we will stay with the %>% operator for some time before switching to |>.
To allow for a piping code, we added several functions: add_l2, select_phases, select_cases, subset, set_vars, set_dvar, set_mvar, set_pvar.

sample_names(): Returns a character vector of length n with names by randomly drawing from a name list: type = {"neutral", "female", "male", "mixed"}. Useful to anonymize scdf files

names(exampleAB) <- sample_names(3)

-add_l2(): Adds the variables from a second level 2 data frame to an scdf matched by an id variable (default is case).

Leidig2018 %>%
  add_l2(Leidig2018_l2) %>%
  hplm(update.fixed = .~. + gender + migration + ITRF_TOTAL*phaseB, 
       slope = FALSE, random.slopes = FALSE, lr.test = FALSE)

select_phases(): selects and recombines phases into A and B phase (equivalent to the phases argument of various functions, but useful when using %>% operators).

exampleA1B1A2B2 %>% 
  select_phases(A = c(1, 3), B = c(2, 4)) %>%
  overlap()

set_vars(): change the core variables of an scdf (arguments dvar for dependent variable, pvar for phase variable, and mvar for measurement-time variable).
set_dvar(), set_mvar(), set_pvar(): Shortcuts to set dvar, mvar, or pvar in a piping script e.g. exmpleAB_add %>% set_dvar("depression") %>% describe()

exampleAB_add %>%
  set_vars(dv = "depression") %>%
  overlap()

is.scdf(): Tests if an object is of type "scdf" or not.
check_scdf(): Checks for the validity of an scdf object (mainly used for internal tests)
convert(): Creates an scdf syntax file from an scdf object.

# Create a syntax to code the scdf exampleAB and write it into an R file
convert(exampleAB, file = "cases.R")

cdc: Applies the Conservative Dual-Criterion Method (CDC; Fisher, Kelley, & Lomas, 2003) to scdf objects.

cdc(Beretvas2008)
cdc(exampleAB_decreasing, decreasing = TRUE, trend.method = "bisplit")

overlap(): Added Hedges-g.
new trend lines added to plot.scdf(): Koenig's bi-split / quarter intersect (lines = "trendA_bisplit") and Tukey's tri-split / Wald's slope (lines = "trendA_trisplit").

plot(exampleAB_50[8], lines = "trendA_bisplit")
plot(example_A24, lines = "trendA_trisplit")

plot.scdf(): Now allows for multiple lines with different line styles.

plot(
  exampleAB, 
  lines = list(
    list(type = "median", col = "red", lwd = 0.5),
    list(type = "trend", col = "blue", lty = "dashed", lwd = 2),
    list(type = "loreg", f = 0.2, col = "green", lty = "solid", lwd = 1)
  )
)

tau_u(): Solved bug in meta analysis #6. Reworked the complete function to be more clear and accurate. Added method_meta switching between fixed and random-effect meta analyses. Reworked the print function to look nicer.
export(): Reworked the html output. Added a basic output for tau_u. Arguments caption and footnote allow to specify appearance (if left NA object specific output is generated.). booktab = TRUE is now set as a default for kable options.

The following functions were deprecated since 2017 and are now removed from scan:

makesingleSC()
makeSCDF(): Please use scdf instead.
estimateSC(): Please use estimate_design()
power.testSC(): Please use power_test()

print.scdf() now prints cases when all variable names are wider than the current screen with.

scan 0.52

describe() as the new alias for describeSC()
plot.scdf(), style_plot(): New options to style casenames: names which takes a list with tag = value structure. Example:

new_style <- style_plot()
new_style$names$side <- 3
new_style$names$line <- -1.7
new_style$names$col <- "darkred"
new_style$names$cex <- 1.5
new_style$names$at <- 20
new_style$names$adj <- 1
new_style$names$font <- 3
plot(exampleAB_decreasing, style = new_style)

plot.scdf(), style_plot(): Different background colors for different phases:

new_style <- style_plot()
new_style$fill.bg <- c("aliceblue", "mistyrose1", "honeydew")
new_style$lty.seperators <- 0
plot(exampleABC, style = new_style)

plot(exampleABAB, style = c("default", "phase_shade"))

scan 0.50.5

fixed bug in plot function

scan 0.50.4

New subset() function (method from base subset() for selecting variables, rows, and cases. It takes the arguments subset, select, and cases.

subset(exampleAB, (values < 60 & phase == "A") | (values >= 60 & phase == "B"))
subset(exampleAB_add, select = c(-cigarrets, -depression))
subset(exampleA1B1A2B2, phase %in% c("A1", "B2"), cases = Pawel:Moritz)

scan 0.50.2

New select_cases() function.

select_cases(exampleAB, "Johanna", "Karolina")
select_cases(exampleAB, 1,2)
select_cases(exampleAB, "-Johanna")

scan 0.50

Started dropping the SC extension from function names e.g. overlapSC() becomes overlap()

scan 0.40

CRAN release 2019-08-11

scan 0.20

CRAN release 2016-10-15

jazznbass/scan_develop documentation built on Sept. 9, 2024, 6:23 a.m.

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jazznbass/scan_develop Single-Case Data Analyses for Single and Multiple Baseline Designs

NEWS.md In jazznbass/scan_develop: Single-Case Data Analyses for Single and Multiple Baseline Designs

scan 0.61.999

New functions / features

Corrections / Changes

Solved bugs

scan 0.61.0

Solved bugs

New

Changes

New examples

scan 0.60.0

New function

Changes

Shiny scan

scan 0.59.0

scan 0.58

Shiny app added

New fucntions

Changes in functions

Bug fixes

minor

scan 0.56

New features

Bug fixes

superseded function

minor changes

scan 0.55

new functions

New features

Bug fixes

Changes to functions

scan 0.54.1

Bug fixes

scan 0.54

new functions

reanmed functions (old functionnames still work)

Complete rework - as new

Extended functions

Solved error in functions

scan 0.53

Major changes

New functions

Changes in functions

Deleted deprecated functions

Bugs

scan 0.52

Major changes

scan 0.50.5

Major changes

scan 0.50.4

Major changes

scan 0.50.2

Major changes

scan 0.50

Major changes

scan 0.40

scan 0.20

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jazznbass/scan_develop
Single-Case Data Analyses for Single and Multiple Baseline Designs

NEWS.md
In jazznbass/scan_develop: Single-Case Data Analyses for Single and Multiple Baseline Designs