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.
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
)An object of class sc_design.
Number of cases to be designed (Default is n = 1).
A list defining the length and label of each
phase. E.g., phase.length = list(A1 = 10, B1 = 10, A2 = 10, B2 = 10). Use
vectors if you want to define different values for each case phase.length = list(A = c(10, 15), B = c(10, 15).
Defines the effect size of a trend added incrementally to each
measurement across the whole data-set. To assign different trends to
several single-cases, use a vector of values (e.g. trend = c(.1, .3, .5)). If the number of cases exceeds the length of the vector, values are
recycled. When using a 'gaussian' distribution, the trend parameters
indicate effect size
d changes. When using a binomial or poisson distribution, trend
indicates an increase in points / counts per measurement.
A list that defines the level increase (effect size d) at the
beginning of each phase relative to the previous phase (e.g. list(A = 0, B = 1)). The first element must be zero as the first phase of a single-case
has no level effect (if you have one less list element than the number of
phases, scan will add a leading element with 0 values). Use vectors to
define variable level effects for each case (e.g. list(A = c(0, 0), B = c(1, 2))). When using a 'gaussian' distribution, the level parameters
indicate effect size d changes. When using a binomial or poisson
distribution, level indicates an increase in points / counts with the
onset of each phase.
A list that defines the increase per measurement for each phase
compared to the previous phase. slope = list(A = 0, B = .1) generates an
incremental increase of 0.1 per measurement starting at the B phase. The
first list element must be zero as the first phase of a single-case has no
slope effect (if you have one less list element than the number of phases,
scan will add a leading element with 0 values). Use vectors to define
variable slope effects for each case (e.g. list(A = c(0, 0), B = c(0.1, 0.2))). If the number of cases exceeds the length of the vector, values
are recycled. When using a 'gaussian' distribution, the slope parameters
indicate effect size d changes per measurement. When using a binomial or
poisson distribution, slope indicates an increase in points / counts per
measurement.
Starting value at the first measurement. Default is
50. When distribution = "poission" the start_value represents
frequency. When distribution = "binomial" start_value must range between
0 and 1 and they represent the probability of on event. To assign different
start values to several single-cases, use a vector of values (e.g. c(50, 42, 56)). If the number of cases exceeds the length of the vector, values
are recycled. The m argument is deprecated.
Standard deviation used to calculate absolute values from level,
slope, trend effects and to calculate and error distribution from the rtt
values. Set to 10 by default. To assign different variances to several
single-cases, use a vector of values (e.g. s = c(5, 10, 15)). If the
number of cases exceeds the length of the vector, values are recycled. if
the distribution is 'poisson' or 'binomial' s is not applied.
Reliability of the underlying simulated measurements. Set rtt = .8 by default. To assign different reliabilities to several single-cases,
use a vector of values (e.g. rtt = c(.6, .7, .8)). If the number of cases
exceeds the length of the vector, values are repeated. rtt has no effect
when you're using binomial or poisson distributions.
Probability of extreme values. extreme.p = .05 gives a five percent probability of an extreme value. A vector of
values assigns different probabilities to multiple cases. If the number of
cases exceeds the length of the vector, values are recycled.
Range for extreme values. extreme_range = c(-7,-6) uses extreme values within a range of -7 and -6 . In case of a
binomial or poisson distribution, extreme_range indicates frequencies. In
case of a gaussian (or normal) distribution it indicates effect size d.
Caution: the first value must be smaller than the second, otherwise the
procedure will fail.
Portion of missing values. missing_prop = 0.1
creates 10\
different probabilities to multiple cases. If the number of cases exceeds
the length of the vector, values are repeated.
Distribution of the criteria varible. Default is
"normal". Possible values are "normal", "binomial", and "poisson".
If TRUE, the start_values are randomized based on the distribution.
If distribution (see below) is "binomial", n_trials is
the number of trials/observations/items.
Number of measurements (in each study). Default is mt = 20.
Phase B starting point. The default setting B_start = 6 would assign the first five scores (of each case) to phase A, and all
following scores to phase B. To assign different starting points for a set
of multiple single-cases, use a vector of starting values (e.g., B_start = c(6, 7, 8)). If the number of cases exceeds the length of the vector,
values will be recycled.
Juergen Wibert
## 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)
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