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). 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).shinyscan()
)options(scan.export.engine = "gt")
. This engine allows to export tables into docx format: overlap(exampleAB) |> export(file = "test.docx", flip = TRUE)
.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
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()
.describe()
: now works correctly for duplicated phase names.shinyjs
and markdown
. shinyscan()
asks if missing packages scplot
and shiny
should be installed automatically. shinyscan()
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())
transform()
: n()
, all_cases()
, across_cases()
, first_of
transform()
: moving_mean()
, moving_median()
, local_regression()
corrected_tau()
: returns multiple casesexport()
: 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)
summary()
failed when one of the cases had no namenap()
only reported values for the first case with multiple cases.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
).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".tau_u()
by 20%.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.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.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)
select_phases()
now works when phase variable is not "phase"estimate_design()
was broken and is fixed now.print()
function for sc_design
fixed.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'
).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 filesAnna:
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.%>%
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 |>
.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 filesnames(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.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.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"))
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)
select_cases()
function.select_cases(exampleAB, "Johanna", "Karolina")
select_cases(exampleAB, 1,2)
select_cases(exampleAB, "-Johanna")
SC
extension from function names e.g. overlapSC()
becomes overlap()
CRAN release 2019-08-11
CRAN release 2016-10-15
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