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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.design(): Argument random_start_values
randomly assigns start values for each case based on the distribution
(normal, poisson or binomial) and
the respective parameters (start_values, s,
n_trials).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 on 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).rand_test(): Missing values in the dependent variable
are now removed before calculations.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
napcoef.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.Parker2009bird(): 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,
markdownbatch_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_oftransform():
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_designfixed.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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